Data Intelligence

Traps to Avoid for a Data Catalog Project – Data Culture

Actian Corporation

September 29, 2022

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Metadata management is an important component in a data management project, and it requires more than just the data catalog solution, however connected it may be.

A data catalog tool will, of course, reduce the workload, but won’t in and of itself guarantee the success of the project.

In this series of articles, discover the pitfalls and preconceived ideas that should be avoided when rolling out an enterprise-wide data catalog project. The traps described in this are articulated around 4 central themes that are crucial to the success of the initiative:

  1. Data culture within the organization.
  2. Internal project sponsorship.
  3. Project leadership.
  4. Technical integration of the Data Catalog.

Organizations with data as the sole product are very rare. While data is everywhere, it is often only a byproduct of the company’s activities. It is therefore not surprising to find that some collaborators are not as aware of its importance. Indeed, data culture isn’t innate, and a lack of awareness of the importance of data can become a major obstacle to a successful data catalog deployment.

Let’s illustrate this with a few common preconceptions.

Not all Collaborators are Sensitive to What is at Stake With Metadata Management

The first obstacle is probably the lack of a global understanding of the initiative. Emphasizing the importance of metadata management to colleagues who still misunderstand the crucial role the actual data can play in an organization is doomed to fail.

It’s quite likely that a larger program that includes an awareness initiative emphasizing the stakes around enterprise data management will have to be set up. The most important element to inculcate is probably the fact that data is a common good, meaning that the owners of a dataset have the duty to make it visible and understandable to all stakeholders and colleagues.

Indeed, one of the most common obstacles in a metadata management initiative is the resistance to the effort needed to produce and maintain documentation. This is all the more of an issue when it is felt that the potential users targeted are limited to a small group of people who already fully understand the subject. When it is understood that the target group is in fact much larger (the entire organization and potentially all staff), it becomes obvious that this knowledge has to be recorded in a “scalable” manner.

A Data Catalog Doesn’t do Everything

A data culture-related issue can also affect those in charge of the project, although this is less common. An inaccurate understanding of the tools and their use can lead to mistakes and cause suboptimal, even detrimental, choices. The data catalog is a central software component for metadata management but it’s likely not the only tool used. It is therefore not advisable to try and do everything just with this tool. This may sound obvious but in practice, it can be difficult to identify the limits beyond which it is necessary to bring a more specialized solution into the mix.

The data catalog is the keystone to documentation and has to be the entry point for any collaborator with questions related to a concept linked to data. However, this doesn’t make it “the solution” in which everything has to be found. This nuance is important because referencing or synthesizing information doesn’t necessarily mean carrying this information wholesale.

Indeed, there are many subjects that come up during the preparation phases of a metadata management project: technical or functional modeling, data habilitation management, workflows for access requests, etc. All these topics are important, carry value, and are linked to data. However, they are not specifically destined to be managed by the solution that documents your assets.

It is therefore important to begin by identifying these requirements, defining a response strategy, and then integrating this tooling in an ecosystem larger than just the data catalog.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.
Data Integration

Connecting Data to Make Customer Experience (CX) Easier

Teresa Wingfield

September 28, 2022

Man showing the result of making a customer experience easier

Effective business decision-making relies on getting CX right can be tricky given that the necessary data is often difficult to access across silos or exists in disparate and hard-to-reach places. A holistic understanding of a customer not only helps businesses know who they are selling to, but it also helps them craft a customer experience (CX) that delights customers and inspires their engagement with the business throughout their buyer journey. Effective business decision-making relies on fresh, in-the-moment data-driven insights. Without a way to connect, manage, and analyze data in real-time, data that sits idly on servers remains unused at a cost.

To make it simpler for data analysts to improve CX, businesses should take stock of a few questions. What tools are needed to create real-time snapshots of our customers? How might we use these snapshots to create more impactful experiences for them, and nurture them? When armed with the right set of tools that can integrate disparate data into a single pane view, data managers will be more equipped to learn and understand customers, observe where they navigate along their buying journey, and find new ways to nurture them. Such actions can help teams form a point of view to help them build stronger engagement, prospecting, and nurturing strategies.

Benefits for the Customer

Businesses must unify customer needs with their own business goals. By unifying those two things, CX teams can offer more timely responses to issues and generate stronger insights on personas and buying behavior.

Throughout the entire customer journey, CX is shaped by data and how easy it is to use and analyze. With simplified CX efforts, customer-centered teams need to create experiences that are highly tailored and relevant to customers.

Customers have the independence to shop and give their business to whomever they want. Customers have more choices than ever when deciding which business they want to engage with and give their loyalty to. This elevates the need for a simplified CX, and the need for creating experiences that continue to be relevant to them. For example, insights that stem from conversations between cross-functional roles such as marketing and sales – can inform tools and messaging that customers interact with. The more we communicate as teams, the more impactful CX is.

Better understanding of a customer’s unique needs through data is a critical practice that businesses can never do enough of. Creating personalized experiences not only can help improve engagement and build trust with the brand, but also helps them avoid potential dissatisfaction that causes churn in the sales funnel. Data alone is not valuable – but when used to assess who customers are, what they’re looking for, and what they care about – CX can establish the means for building trust and usefulness at each step of the buying journey.

When it comes to pricing and packaging, using available data on purchase patterns creates opportunities to experiment with different pricing strategies and marketing engagement campaigns. Should we test out our new pricing strategy? How might we package our capabilities and see how our customers react to it? These types of insights are critical to getting a complete 360-degree picture of customer behavior and for CX teams to tweak the experience on a customer-by-customer basis.

Benefits of CX for the Ambitious Business

Building memorable and high-impact customer experiences is a win-win for customers and businesses. By having clarity on their customers’ journey, businesses can develop targeted experiences, offers, and tailored campaigns from the awareness stage to the decision-making stage. In addition, businesses can use what they learn from this discovery journey to create new revenue streams. Businesses can also streamline the CX journey, increasing bandwidth to engage both new and existing customers.

Quick and easy access to trusted, real-time data lets businesses proactively engage in customer needs – not just responding to them reactively. Getting CX right can be a tricky task but can be simplified by focusing on connecting data to CX teams looking to boost experiences for customers.

At each stage of the customer journey, the CX footprint should be persistently present. Streaming, structured, and unstructured data processing, along with orchestration and automation enables customer experience professionals to do what they do best. Investing in efforts like building customer retention models and customer acquisition strategies helps yield measurable, impactful outcomes for businesses.

Actian Data Platform serves as a single platform for integration, management and analytics that combines zero-, first-, second- and third-party data. This provides businesses with real-time insights and access to hundreds of data sources, without performance disruption.

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About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

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Data Integration

How to Get More Out of Your Data Integration Strategy

Actian Corporation

September 26, 2022

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Conceptually, data integration sounds like a simple task: taking data from multiple sources and combining them to help inform business decision-making. This idea has existed since enterprises have been using datasets.

However, as data continues exploding in both volume and complexity, enterprises can no longer rely on manual integration processes. Instead, they must use the right integration tools for the job – which is complicated by the myriad of vendors and solutions, each with its own purpose-built capabilities.

To properly tackle data integration, business leaders must be clear on what they hope to achieve with a data integration strategy. Without a solid view of goals and desired outcomes, businesses can run into issues like duplicate data, data governance, and compliance issues. After all, not all data is created equal.

To avoid this, we will examine how businesses can mitigate some common data integration challenges and kickstart their data integration strategy.

Eliminate Any Data Silos

Businesses need to have solutions in place that can sync all datasets from across the company, creating a master record of data that spans all of the different systems within an organization. This requires the use of a data integration tool that can ensure data quality and possesses some workflow capabilities to automatically check for duplications. These tools should easily locate and remove duplicates, standardize formats, and share data from one system to another.

Leverage Your Partners for Better Integration

In the past, partners may have simply faxed the relevant information, and enterprises would re-input it into their systems. But this method is time-consuming and error-prone. While many organizations still rely on electronic data interchange (EDI) to integrate their data, modern technology offers several alternatives, such as data transfer via Web services that rely on XML files or extensive use of APIs. Other companies use more than one method to transfer data between partners.

Find the Right Data Ingestion Product That Can Pull Data From a Variety of Sources

Across industries, organizations have one or more repositories for the data from which they hope to glean valuable insights. However, all of this data must first be collected in one place, then properly cleansed and formatted for analysis. Many companies rely on a data warehouse, data lake, or combination of the two to store data, and the type of data integration will depend on which platform you are using. For example, you may need ETL tools (extract, transform, load) if you are using a data warehouse; a data lake will need a different data migration product to pull data from different sources.

Getting Integration Underway

To ensure that integration goes smoothly, there are some parameters that must be first met, specifically around governance and data management. Enterprises must ensure that data that is meant to be integrated meets privacy regulations, such as local data privacy laws and internal standards.

Additionally, there must be appropriate rules in place for who can use the data, as well as a plan for risk reduction when moving the data between systems. Once the proper governance structure is in place, then data stewards must ensure that the quality of the integrated datasets is well-regulated and managed according to the needs of both the business and its customers.

Most importantly, they need to have a plan for how users will access usable and accurate data. Data is of little value if it cannot be accessed by those needing to make decisions. With this information clearly spelled out, businesses can set themselves up for success as their data integration plans are underway.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

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Data Management

What is the Future of Data Quality Management (DQM)?

Teresa Wingfield

September 22, 2022

icons representing the future of data quality management (DQM)

Today’s digital-first world has placed a tremendous emphasis on using enterprise data intelligently to drive important business decisions and manage risk. High-quality datasets function as the fuel for enterprises looking to map customers, track where they are in the lifecycle, and inform the next steps on how to better engage them.

However, simply having access to high-quality data is not enough to gain a competitive edge. Businesses need to have robust data quality management (DQM) strategies in place if they want to use datasets effectively. Organizations that do not emphasize these may end up with little to ineffective business outcomes, despite having high-quality data available.

For data stewards and managers, now is the time to look holistically at the systems and processes used internally to drive data quality management strategies and determine what the future holds for their role.

What is Data Quality Management?

DQM is a collection of the processes, tools, and knowledge associated with data to drive forward business decisions in a flexible and agile way. When done effectively, it improves the efficiencies of processing data flows and ensures that businesses can realize their maximum potential of datasets.

Data stewards can help businesses better communicate across siloes, standardize the way data is handled, and make governance models more robust by clearly defining the purpose and intent of data.

These programs have evolved over the years from a highly technical area, often managed by a single team or department, to an enterprise-wide effort involving a host of players. The structure and scope of these programs vary from business to business, but the goal remains the same – to bring greater awareness to the importance of using high-quality data to drive decision-making.

This full-team approach toward driving better data management has been crucial for businesses over the past few years, especially given the explosion of data creation since the beginning of the COVID-19 pandemic. Unprecedented events such as this forced many companies to throw their data management playbooks away (or quickly create one) and adapt to rapidly shifting consumer behaviors – including how they shop, where they live, and how they work. These shifts produced waves of new datasets that organizations need to analyze and use to ensure they stay competitive in a changing business landscape.

What Does it Mean for the Future?

The continued explosion of data will mean that as data stewards define their future data management plans, they must account for volume. Simply put, an enterprise can no longer rely on a single department or set of individuals to process wave after wave of datasets. This has raised a need for efficiency to ensure businesses can process data while maintaining its quality and value.

To assist with volume processing, and future influxes of data, DQM plans need to include a level of automation that can help filter out quality data and better integrate it across the enterprise. Automated processes can assist with data profiling, cleaning, parsing, and deletion, as well as backing data up on a regular cadence. Continuous data monitoring offered by automated technology will also assist enterprises in finding issues within their datasets more quickly than doing so manually, better positioning businesses to solve problems faster.

Additionally, the future of DQM will be increasingly collaborative. With an emphasis on better data integration across the enterprise, more teams will be able to align on shared goals and priorities. Data managers can create tools that stewards on other teams can use to observe and measure the quality of their own data. This collaborative setting will allow for greater knowledge-sharing and transparency across the business regarding how data is stored and used.

As data continues growing in complexity and scale, it is important for business decision makers to prioritize effective DQM strategies for today and for future organizational growth. If businesses can’t process their high-quality data in a way that is both adaptable and scalable, they will not be prepared when another disruptor or volatile market event comes along.

Discover how you can more easily deliver high-quality data at the speed of business.

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About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

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Data Intelligence

What is Data Ingestion?

Actian Corporation

September 20, 2022

Data Ingestion

Relying simply on intuition is no longer possible – to gain a competitive advantage, it is essential to elevate your data-driven strategy. With Data Ingestion, you can access information faster and more efficiently by centralizing it in a single location. Here is an overview.

In a hyper-competitive and ever-changing business world, companies are in a race against time. A race that does not necessarily oppose their direct competitors but rather their customers. The challenge is to identify consumer trends to anticipate their expectations. Being the first to satisfy a given need or to enter an emerging market. These strategic conditions can be met through Data Ingestion.

Data Ingestion allows you to have an even finer knowledge of your customers and your market through the exploitation of increasingly heterogeneous data, and identify weak signals in order to anticipate trends quickly, but above all efficiently.

Understanding Data Ingestion

The principle of Data Ingestion is based on the idea of centralizing different sources of data in one place. By nature, this heterogeneous data needs to be meticulously cleaned and deduplicated to be brought together in a target environment for processing and exploitation. Whether your data comes from a data lake, customer files, or SaaS applications, it can be aggregated within a target site in order to reconcile it with one goal: to improve the understanding of a market, an ecosystem, or a target.

The term reconciliation perfectly sums up the purpose of Data Ingestion. The aim is to combine the knowledge contained in different types of databases to maximize the lessons learned.

What are the Main Benefits of Data Ingestion?

If you decide to embark on a Data Ingestion project, you will be able to reap various benefits. First, you will inevitably gain responsiveness and flexibility. Indeed, Data Ingestion tools are able to manage and process not only very large volumes of data but also a wide range of data types, including unstructured data. Data Ingestion also promises simplicity. With its ability to reconcile disparate information sources, Data Ingestion makes it much easier to extract data and restructure it into predefined formats to make it more usable.

The information that Data Ingestion gives access to can then be leveraged within advanced analytical tools. Maximizing the benefits of this in-depth knowledge of your customers or market will feed BI tools and make it easier for you to take a step back and define new strategic directions. Indeed, Data Ingestion contributes to simplifying access to data for your employees.

A more developed data culture also means faster and more informed decision-making and, consequently, a competitive advantage in defining more effective tactical and strategic levers.

What are the Challenges of Successful Data Ingestion?

Data Ingestion remains demanding, and a certain number of conditions need to be met to deliver its full potential. For example, ingesting very large volumes of very different data can raise data quality issues that can not only degrade the relevance of analyses but also lengthen processing times. In addition, the diversity of data sources mechanically increases exposure to vulnerabilities.

More complexity and more exposure to risk mechanically lead to a risk of increased processing costs. To succeed in a data ingestion project, you need to be aware of these risks in order to know how to protect yourself against them.

How to Succeed in a Data Ingestion Project

The first piece of advice for an effective Data Ingestion project is to anticipate. Your ability to anticipate risks and difficulties depends on the proper mapping of your data assets.

Another lever is to activate automation. The volumes of data processed by data management are so massive that manual operations must be limited to a minimum. Automating the processing of information also has the advantage of providing more consistency in the structure of your data.

Finally, to maximize the chances of success of your Data Ingestion project, you can also consider opting for real-time Data Ingestion. Also known as Streaming Data Ingestion, it is particularly suitable when you are looking to constantly update the knowledge you have of a market. This real-time ingestion provides a key answer to real-time decision-making issues.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.
Data Analytics

Prioritizing a Customer Experience Strategy for Business Growth

Teresa Wingfield

September 20, 2022

Man reviewing the opinions of his clients to understand and improve the customer experience

It’s no longer a question if business leaders must understand their customers and be able to identify where they are navigating in their technology journey. Creating a solid CX is not a luxury, but an essential practice for ambitious businesses that want to connect with their customers in meaningful ways – and this starts with learning, and trying to understand the many dimensions of their customers.

In a digital-first world, data is widely available to businesses looking to improve their CX, and every interaction a customer has helped to paint a full, 360-degree picture of who they are. Stitching together this picture takes effort, research, and great diligence – and when done right, the rewards of building a deeper view of who these people are help businesses make the connection in more humanly, tailored ways.

A strong CX approach informs the frame of thought and strategic approach used to build out retention and growth strategies. It is worth addressing what CX is, why it’s important, and to how to work through the hurdles of creating an exceptional CX.

What is CX?

“Customer experience” may sound like a broad, ambiguous and simple concept, but it is important to understand the purpose that it serves. In an enterprise, businesses use CX to engage with their customers throughout the buyer journey. The experience and means of delivering CX is more than just having touchpoints with multiple business teams – including marketing and sales, customer service and UX.

CX embodies a curiosity to truly understand customers. In a business context, we often overuse the word, which can diminish the humanity of building a customer connection. Who are our customers and what do they need? What are they thinking about? What would a moment in time in their professional or personal life look like? How do our customers engage with our business – and what will we as a business do to make these connections possible? What will we do to keep learning about them? How will we make their experiences memorable and build their trust in us? Questions like these are just the beginning – but a significant start.

CX is not focused squarely on customer actions, as it also takes feelings and emotions into account to paint the full picture. Each interaction offers businesses the opportunity to learn more about their customers: their buyer personality, behavior, recurring trend on actions they take, and how they make decisions. All these things help to improve the experience for the customer.

A strong CX strategy is a vital goal to keep on top of mind for organizations looking to grow, and those efforts require steady effort, and creative ways to deliver on actions and test ideas for the sake of learning more about their customers (and prospects). As a result, siloed data that does not provide a holistic look at customers can sink a CX strategy before it even begins.

What Holds Back Positive CX?

One of the biggest barriers to an effective CX strategy is disparate data siloed across departments. Legacy CRM systems and data warehouses are ill-equipped to handle the vast amounts of new data generated daily to create a picture of the buyer persona.

CX managers need real-time access to data so that they can identify and search for touchpoints along the customer journey in any given moment. Customer profiles built on diversified data can come from any number of channels. With data that’s siloed across the organization, those channels may be hard to access, or completely inaccessible.

Another common hurdle with CX efforts is not being able to view all the data in one place. Having the data alone will not improve CX; it is how that data is analyzed, and how those insights are unlocked from one central location. When pulling from multiple data sources, if data can be viewed under a single pane, one can have an expanded view. Without that crucial aggregation, analytics tools will miss data and will not be able to convert it into actionable insights.

Painting the Whole Picture

Taking a 360-degree view of the customer and using data from multiple sources is one of the best ways to boost CX. Using data to create actionable steps will require businesses to have a cloud data platform that offers more than a view of the customer and data – it must help businesses have the right tools and capabilities in place to capture a full snapshot of their customers.

Platforms like the Actian Data Platform dive deep in the realm of analytics and open new data sets for marketers and other CX teams to analyze and turn into a 360-degree view. The Actian Data Platform helps businesses understand where their customers are now, and where they are going in the future by using analytics to interpret feedback. The platform also uses customer sentiment, helping businesses form a more thorough view of what their customers may need and feel.

Here at Actian, we are committed to providing integrated, 360-degree customer data that gives businesses the opportunity to have a comprehensive insight into who the customer is and how to reach the customer. Ambitious organizations that incorporate customer empathy in their business and prioritize a memorable CX experiences will come out ahead in capturing the hearts and minds of their customers.

Read all about the Actian Data Platform and how it delivers a personalized, consistent CX.

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About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

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Data Platform

The Value of a 360-Degree Customer View

Actian Corporation

September 15, 2022

Image of a dark blue network of people profiles giving an example of 360-degree customer view

Embracing changes in customer demands is an essential quality of a forward-thinking enterprise. Customer demands are always evolving, and the past few years have turned consumer behavior on its head, making agility the biggest competitive differentiator. So how are companies supposed to keep up with customer preferences?

Determining what behavior will become long-term shifts depends heavily on consumers’ openness to embracing new experiences versus reverting to pre-pandemic behaviors. As more customers increasingly expect a real-time, personalized, data-driven experience, companies are realizing they must prioritize building and nurturing more purposeful relationships with customers in order to set themselves apart competitively. In fact, our recent report with 451 Research reveals that nearly 84% of respondents believe transforming to a real-time enterprise is important to meet customer demands. With a 360-degree view of the customer, business leaders can stay apprised of changing behaviors and ensure that the same truth is accessible to everyone in the company.

The Benefits of a Holistic 360-Degree Customer View

As consumer behavior changed alongside the pandemic, our report with 451 Research shows that 81% of businesses believe generating a 360-degree view of the customer to understand intent and context will have a high impact over the next two years.

Achieving a holistic view can provide numerous benefits in this data-driven customer experience economy, such as:

  • Customer Loyalty: Most customers prefer to buy from a business that caters to them. Predicting their needs and tailoring the experience will help build customer loyalty and more trust over time.
  • Improving ROI: Using data aggregated in the 360-degree view can help your business make more purposeful touchpoints with customers. Knowing their preferences and general behavior will also help improve marketing campaigns, thereby increasing the bottom line.
  • Collaboration Across Departments: Sharing the same customer view across departments can help teams communicate with each other more effectively so that they can work collaboratively on the customer efforts that matter. From sales and marketing initiatives to nurturing campaigns – collaboration is key.

To reach these goals and enjoy their benefits, companies must aggregate data from a variety of touchpoints that consumers use to interact with the business and make purchases, cataloging and interpreting that data for insights. However, many businesses find it challenging to harvest data.

Barriers to Entry

There is an insurmountable amount of data that flows through touchpoints and events that companies collect and analyze across public cloud, private cloud, and on-premises infrastructure. Collecting, managing, and making sense of this data is critical to understanding the complete customer profile. However, our research reveals that roughly one-third of organizations (32%) say they have 7+ silos of customer data. If data is that siloed, it is challenging to aggregate and understand all of the data points needed to achieve a unified customer view.

For many IT departments, it is difficult to conceptualize the amount of data points that must be considered when aggregating the data needed for a modern, 360-degree view. As businesses innovate and improve their infrastructure and AI/ML applications, learning how to harness this data is more important than ever to truly capture the potential of these innovations.

Achieving a 360-Degree Customer View

Harnessing the power of data is necessary for any company that wants to personalize its customer experience using a 360-degree view. Businesses must consider all tools, processes, and data from start to finish. This helps maintain a storehouse of all knowledge of customer behaviors and preferences to tailor the experience for each customer. Additionally, businesses must capture, analyze, and actualize the information they collect, including recognizing patterns and solving problems at scale. Once achieved, a 360-degree view of the customer will help businesses anticipate customer needs, personalize experiences, and stay ahead of competitors.

The Actian Data Platform with its Customer 360 Real-time Analytics solution runs on AWS, Azure, and Google Cloud platforms. Data can be migrated easily to the Actian Data Platform , enabling users to stay connected to tools they use to analyze and visualize Customer 360 data.

For more information, please read about Actian’s Customer 360 real-time analytics solution or view the solution brochure.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

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Data Intelligence

What is Data Intelligence?

Actian Corporation

September 15, 2022

Data Intelligence

The term Data Intelligence refers to all the methods and processes that contribute to the collection and analysis of data to identify key trends that can be used to understand a market or ecosystem. In other words, Data Intelligence consists of refining a large volume of raw data to turn it into exploitable and valuable information.

In this article, you will discover all you need to know about Data Intelligence, from its definition to the advantages it can bring to your company.

Understanding Data Intelligence

Companies undergoing a digital transformation collect and generate large volumes of data. This data from different sources (sometimes third-party data sources) is important to collect and essential to classify, understand, analyze, and thus enhance. This is the very definition of Data Intelligence. This discipline, which is part of Data Science, aims to identify, via raw data, value-added information likely to facilitate decision-making in an organization.

The raw data collected must be considered a precious resource that must be transformed and refined to reveal all its subtlety. Like a diamond in the rough that needs to be cut, Data Intelligence reveals the information to be extracted from the data. Data Intelligence can, for instance, be used to identify growth or development opportunities for a company, predict the impact of economic changes on an ecosystem, or get ahead of competitors regarding new market trends.

Considered a strategic asset to gain operational efficiency, Data Intelligence cannot (and should not!) be confused with Business Intelligence.

What are the Differences Between Data Intelligence and Business Intelligence?

Regarding Data, disciplines sometimes tend to overlap and tangle in their names and vocations. For example, we often mistake Data Intelligence with Business Intelligence, which has very different goals. Indeed, Data Intelligence aims to create the conditions for structuring data assets with a view to subsequent exploitation. Business Intelligence, on the other hand, is a process that adds value to data once it has been refined by Data Intelligence.

To summarize, Data Intelligence contributes to organizing the available information within the company, and Business Intelligence organizes the company’s activity according to the available information. Therefore, Data Intelligence is a discipline that is firmly oriented towards the elaboration of future strategies (to identify investment opportunities for example), whereas Business Intelligence is based on the understanding of operational processes.

In any case, Data Intelligence and Business Intelligence remain intrinsically linked because they both contribute to making your company’s decision-making processes more fluid.

The Company Objectives for Data Intelligence

Are you considering embarking on a Data Intelligence project? To do so, you need to set a clear roadmap and define clear company objectives. Data Intelligence allows you to analyze your organization’s performance and implement corrective measures where improvements can be made.

Because Data Intelligence relies on the analysis of a wide range of data, it allows for advanced correlations to create extremely fine-grained, in-depth analyses. For instance, if your company has a strong online presence, Data Intelligence will also allow you to understand and anticipate your customers’ behavior by exploiting navigation cookies.

Customer journey optimizations, conversion rate improvement…Data Intelligence allows you to save precious time. A reality that translates into a major competitive advantage.

What are the Benefits of Data Intelligence?

It’s in your best interest to leverage Data Intelligence to better leverage your data assets. Indeed, the first benefit of Data Intelligence is the acceleration of decision-making processes. Forget intuition and risky bets: decisions are based on real-time observations made and quality data.

Data Intelligence also means reducing your risk exposure so you can make decisions based on the knowledge you gain from your data. Less risk of error in strategic decisions also translates into better cost control. Data Intelligence helps identify duplicate data, and unnecessary steps in the analysis or decision-making processes that are detrimental to your company’s productivity.

Data Intelligence allows you to be both more effective and more efficient. Finally, it is a promise to take a step back from your business to identify tomorrow’s trends before your competitors do.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.
Data Analytics

Customer Data Analytics Tactics of 2022 and the Future

Actian Corporation

September 13, 2022

Customer service agent surrounded by digital information and customer data analytics tactics

Customer relationship managers have the difficult task of knowing where customers are in their lifecycle and knowing exactly what their shifting needs are. With the first half of this year in the rearview mirror, now is an important time to take stock of customer data analytics priorities, and how far we’ve come since the beginning of the year.

In our joint webinar with Looker hosted earlier this year, we outlined what is new in the realm of customer data analytics, along with top priorities for data managers looking to simplify analytics initiatives and deliver better, personalized experiences to their customers in 2022. Now that the summer is wrapping up, let’s take a look at where we started the year and where we expect to be by the end of it.

Customer Data Analytics Today

“Customer data analytics” carries a broad-reaching definition. Businesses may leverage it to gather information about customers to better understand their audience demographics. Others may use it to find the exact moment a customer resides in the buying cycle. Organizations aiming to improve their analytics approach first need to define their goals, then they can work with the available data to inform the next steps and improve the customer experience.

Business agility increasingly depends on data-driven analysis to create a complete, 360-degree view of customers and the best way to reach them. This means businesses need easy access to fresh and diverse data to help them create a full picture of their customers.

This need aligns with the initiatives cited at the onset of 2022 by IT leaders as their top priorities – enhancing analytics and business intelligence efforts and improving data integration. The need for improved analytics and integration has been ever-present since businesses began collecting and analyzing data on their customers. To create better experiences for their customers, businesses need to make the most of their data to develop a thorough understanding of customers and how to better serve them.

At the time of our webinar this past January, businesses outlined their top customer and marketing analytics data projects. These projects revolved around three key elements of customer marketing strategies: acquisition of new customers, retention of existing customers, and the expansion of data sources. For many, the top projects within those buckets were 1) to create a system of unified customer data 2) to improve know your customer (KYC) initiatives, and 3) to see overall advancements in the realm of customer analytics.

However, as the year has progressed, the focus in these areas has been refined and sharpened to pinpoint specific areas in which businesses can better integrate this data into their business intelligence efforts.

Improving for the Future

The initiatives outlined above are still top of mind for analytics teams looking to create better experiences for their customers, but a sharper focus on these areas is needed if organizations are going to up-level their analytic approach.

New research from Foundry uncovered that nearly 90% of IT decision-makers agree that the proper collection and analysis of data has the potential to significantly change how they do business over the next few years. Nearly half of those IT decision-makers say the improvement of customer insight and engagement is a key driver for data-driven initiatives. This signals the need for analytics tools to be integrated holistically throughout the business to ensure easy access to data across teams and departments.

A recent McKinsey survey found that customer care is the number one priority for businesses looking to establish better relationships with their consumers. Specifically, the goals for customer data analysis include retaining and developing talent and driving a smoother customer experience (CX), all while lowering costs and building more advanced analytic ecosystems.

Another pressing need for businesses is to overcome challenges to their own data initiatives. Foundry found that organizations are in serious need of many skillsets: analytics training, data management, data security, data integration, and business intelligence. Up-skilling and training employees is crucial, especially as talent shortages continue to ravage IT teams.

These new priorities may be daunting, but they are now mission-critical, as organizations are under more pressure than ever to understand and cater to customer preferences and purchasing behaviors.

Take a Real-Time Approach

To effectively leverage the data that exists within an enterprise, businesses need tools that can provide real-time analysis of customers, as well as a single source of truth for quick access to insights. The integration between Actian and Looker accomplishes this by providing IT teams with in-database architecture that offers real-time data insights from the Actian Data Platform, augmented by in-memory performance from Looker.

All data in The Actian Data Platform can be made available to users in a customized and user-friendly way, and data can be explored freely and quickly. Advanced Actian functionality can be directly leveraged in the LookML modeling layer, offering user-defined functions, as well as JSON querying.

Whether a business wants to improve the way they acquire customer data, or better integrate that data within their systems, it’s crucial they focus on the needs of their consumers to create superior experiences for them. In the second half of this year, all eyes will be on how businesses handle their customer data analytics, and those that can paint a 360-degree view of their target markets will be the best positioned as we head into 2023.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

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Data Intelligence

What is Cloud Security?

Actian Corporation

September 12, 2022

cloud security

Cloud Security refers to the technologies, policies, controls, and services that protect data, applications, and infrastructure in the cloud from both internal and external threats.

Between essential protection and uncompromising lockdown, it is critical to find the best compromise between security practices and the flexibility that is essential for business productivity.

Business applications, data storage, complete virtual machines… Almost everything can be governed by Cloud Computing. According to forecasts and observations made by IDC, it appears that spending on cloud infrastructure will increase by 22% in 2022 compared to 2021. This means that it will exceed the $90 billion mark by the end of the calendar year. A record number as this is the highest annual growth rate since 2018! But the more our businesses become cloud-based, the more the issue of Cloud Security becomes a top priority.

However, behind a concept as vast and complex as Cloud Security, it must be understood that it is based on a set of strategies, technical means, and control solutions that ensure both the protection of data stored, the availability and reliability of applications and infrastructure services essential to the operation of the cloud.

Protection against external and internal threats and vulnerabilities, resilience, and resistance to cybersecurity issues, Cloud Security is a concept intrinsically linked to Cloud Computing.

Private, Public, Hybrid…Each Cloud Has its Own Security Challenges

To fully understand Cloud Security, we must first differentiate the types of Cloud Computing. Public clouds are hosted by third-party cloud service providers.

When you use a public cloud, you get a turnkey cloud and have no latitude to configure and administer it, and the services are fully managed by the cloud provider.

If, on the other hand, you move to a private cloud service, you have a more secure and potentially more customizable space.

Finally, hybrid cloud services combine the scalability of public clouds with the greater resource control of private clouds while offering lower pricing than private clouds.

In all cases, whether you choose a public, private or hybrid cloud service, it is critical to ensure Cloud Security.

Why is Cloud Security Important?

The more your company leverages the cloud, the more agile it becomes. Even better: the cloud enables small and medium-sized businesses to have the same tools and functionalities as very big companies. The downside is that the power at your disposal increases the cloud usage of your teams and, consequently, increases your exposure to threats that in the past only concerned larger companies.

That’s why Cloud Security is more important than ever. Unleashing usage and productivity through the cloud mechanically increases your dependency on the cloud.

Without the cloud, nothing is possible. Therefore, Cloud Security becomes a priority issue, especially when it comes to data protection. Preventing data leakage and theft is essential to maintaining your customers’ trust. Cloud Security, by guaranteeing data protection, makes it an element of trust between you and your customers.

The Challenges of Cloud Security

The availability of the cloud is a major issue for companies. In this context, the first challenge of Cloud Security is to guarantee maximum availability, particularly by protecting infrastructures from Denial of Service (DDoS) attacks. Analysis of traffic on cloud servers, and detection of suspicious packets, are all practices that are essential to Cloud Security. The fight against data breaches and by extension, against data loss, are two other prominent challenges of Cloud Security.

Finally, the last key challenge of Cloud Security is user identity verification. As employees become increasingly mobile, they connect to cloud services from anywhere in the world, making identity verification more and more complex. This is why multi-factor authentication is highly recommended by cloud players.

Anticipation, visibility, transparency, reactivity, and proactivity are the levers to be used on a daily basis to guarantee Cloud Security.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.
Data Security

The State of Data Security Management in 2022

Actian Corporation

September 8, 2022

Hand pointing to digital padlock signaling the importance of state of data security management in 2022

With the current threat landscape, securing information is a high priority for any business with a digital footprint. Cybersecurity threats like ransomware, spyware, phishing, and other malware attacks have become daily occurrences and are increasingly becoming more sophisticated, targeting the lifeblood of any business – its data.

The security of the enterprise is incumbent on the protection of all data associated with the business and user. As work-from-home setups have introduced a multitude of personal devices – such as cell phones, laptops, tablets, and wearables – into the working environment, users are exposed to more points of vulnerability. These devices are often unsecured and exposed to unwanted risk, making it easier for hackers to access valuable data that’s protected within the walls of the enterprise.

Navigating the choppy waters of cybersecurity is tricky. Data security management strategies are a must-have for businesses looking to keep their data safe, secure, and out of the hands of bad threat actors. To understand how to effectively deploy a data security management strategy, businesses must first have a firm grasp on what it is and what it entails.

Data Security Management, Explained

Simply put, data security management is the practice of ensuring that data, no matter its form, is protected. Each business must clearly define its data security program goals and communicate them broadly across the enterprise to ensure all teams know how to handle a cybersecurity event.

Data security acts as the safeguard of data while an organization is storing and using it. Data privacy is the practice of ensuring the data that is stored and used is compliant with standards set by regulatory bodies and internal policies. Security keeps data safe, while privacy ensures confidentiality.

Data security management practices protect users and organizations from unintentional mistakes or hackers that would corrupt or steal your precious resources. Before developing a new strategy, businesses must also understand the top challenges associated with data security, as well as the types of threats that currently exist.

Challenges, Risks, and Threats

Businesses must remember that risks and threats exist internally and externally. A recent report found that poor passwords or credential management, as well as misconfigured cloud data storage, are among the top causes of security breaches.

Having a complete view of where internal data flows in and out of an organization is challenging. Without that clear insight, there may be unintended ways that individuals and teams handle and protect their data. Without proper guidance as to how these systems work, there’s a risk of data mismanagement, creating security gaps where threat actors can attack.

This issue can be compounded by the risks of working from home on devices that aren’t sanctioned by IT teams and business leaders. When personal devices are introduced into the network, any vulnerability that already exists on the device is brought into the fold. This may include improperly using email or social media to share data, as well as the use of other unsanctioned applications, resulting in SaaS sprawl. Additionally, employees using a personal hotspot or public Wi-Fi can invite threats, as these are much less secure than corporate networks.

Another challenge with remote work is a workforce distributed across locations and devices. Monitoring how employees are using and interacting with data and ensuring that their data is safeguarded is critical. Organizations need to know where data is coming from, how it’s created, and how it’s being managed. Privacy issues are a concern if data is not being stored in a way that’s compliant with regulatory laws and internal policies.

Properly understanding the challenges and threats that exist can help a business chart a course towards building an adaptable and effective data security management system strategy.

Adapting Security for Your Enterprise

When building data security management, it’s crucial to know these are not one-size-fits-all solutions. There are many different types of data security management strategies that an organization can choose from based on the needs of the business. There are three strategies that your enterprise can examine:

  • Encryption Keys: Encryption keys transform data into unreadable formats via an algorithm that aids in designing services and can proactively prevent security attacks. Introducing various types of data encryption requires skilled data security measures from trained staff or trusted supplier partners. Taking this route is like holding onto a house key. If an encryption key is lost, its crucial to have a seconder holder of the key should the primary holder be unreachable.
  • Organizational Data Security Management: In this strategy, security roles are assigned to data stewards, administrators, product owners, developers, or other stakeholders. This practice creates a culture of security within the company and can help spread security knowledge-sharing across the organization.
  • Data Deletion, Erasure, and Destruction: The use of software to eradicate data deliberately and completely from a storage device (digital or physical) under the direction of the data owner, data steward, or governance team.

When deployed properly, these strategies can help any organization address the current threat where it lies and prevent the damage of cyberattacks before they can begin.

These challenges may seem daunting, but the risk of being exposed to a cyberattack is worth putting in the time, budget, and effort to secure and protect an organization’s data. Businesses should consider taking a full audit of the data that exists in the enterprise and learn how that data is being accessed by a workforce that works both on-site and remotely. An audit will also help create a comprehensive understanding of where potential security gaps live and where there are opportunities to mitigate those security risks. Once identified, businesses can communicate information security best practices and polices across the organization.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

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Data Intelligence

7 Obstacles to Democratizing Data Access in an Organization

Anthony Corbeaux

September 7, 2022

7 Obstacles To Democratizing Data Access

Many organizations have the objective of becoming data-driven, i.e., basing their strategic decisions not on hunches or trends, but on accurate, reliable data and analysis. This implies a process of storing, documenting, and making available this data to make the most of it. If these companies equip themselves with modern tools to democratize access to data, they are faced with a multitude of difficulties that can slow down the process. This article is based on our experience with the Actian Data Intelligence Platform users from organizations of various sizes and sectors to describe 7 obstacles frequently encountered on the road to data democratization.

Tools are not Sufficient

Among the users of the Actian Data Intelligence Platform solutions, the democratization of data and the desire to switch to a data-driven decision-making model are, of course, major priorities. Moreover, access to the data of these organizations is partially democratized since they are all equipped with dedicated tools like data lakes and data labs. Naturally, the deployment of a Data Catalog in these companies is also an illustration of this, with the use of a unique platform capable of centralizing an entire data ecosystem that is shared with all employees.

These tools are essential building blocks for any data-driven approach, but they do not, on their own, make access to data more democratic. If we take a data catalog, for example, the tool becomes especially effective when it is used by the largest number of people in the organization. It is the multiplication of use cases and the documentation of data assets by as many employees as possible that allows the value of the company’s information to be unlocked. Everyone at their own level can then benefit from the work of their colleagues, a virtuous circle in short. To encourage this, a cultural change is necessary.

Shifting Corporate Culture

There is sometimes a lack of awareness of the value of the data available in the organization and a lack of commitment to the process of documenting and sharing data. The challenge lies in the use of the tools mentioned above, with data often remaining in silos between the different departments and teams. This mindset is even more difficult to change at the business level, whereas IT teams are culturally more aware and inclined to document and share data.

Governance units were created to promote this awareness, but the lack of legitimacy within the organization complicates their work of raising awareness of the central role of data for the company. In data mesh literature, it is recommended to federate/decentralize data governance. Business teams must be integrated into this process, at the risk of creating a language gap: governance teams must work with data owners, data engineers, data analysts, etc. The democratization of data access must involve both data producers and consumers.

The notion of changing the company’s mindset is a necessity to complement the tools in place to democratize data. Research published by Gartner shows that historically, organizations have evolved into a defensive culture of “never share, except” for good reasons to share it. The research institute insists on the need to switch to an “must share, except” philosophy. Tools (data lakes, data labs, data catalogs, etc.) are not enough to democratize data if they are not supported by this cultural shift.

Documenting After the Fact

Many projects are primarily driven by costs and time, and in these cases, data governance and data quality are typically not priority topics from the start. There is a tendency to document after the fact, making sharing and documenting data more difficult. Data quality, and even more so its documentation, is all too often the last task to be executed.

The Lack of Time

The lack of documentation is a bias that is heightened in organizations whose products and value are created through the exploitation of data – where the obstacle to democratization is more related to the lack of time for documenting than a lack of data culture as mentioned above. If we go back to the example of a data catalog and focus on the data scientist profession, we can see that this type of population has more or less the desire to document its activity but does not take the time to do so, since the completeness of the data catalog is not a priority.

Furthermore, documenting and making data available is not always part of the employees’ mission. There is therefore also an HR dimension to data democratization. The documentation mission can be added to the scope of the employees’ responsibilities to promote democratization and accountability.

The Volume of Data

A form of fear sometimes arises when contributors are asked to share their own business data within a large common container (a data lake or data catalog). This is the fear of finding oneself drowning in an ocean of data added by other entities of the organization, and of not being able to find one’s way around.

The data catalog is a valuable tool to alleviate this fear among data producers. Indeed, the tool offers them the possibility to not only easily explore their own data, but also to use data produced by others for their own use cases.

Data Security

The security aspect regularly comes up as a pretext for not sharing data within the company. However, there are effective systems for managing user permissions, such as the one integrated into the the Actian Data Intelligence Platform data catalog, for example, which, coupled with a culture of sharing and accountability, can make it possible to overcome this barrier.

Data Ownership

As far as the notion of ownership is concerned, we too often observe ownership of datasets at a local level. Yet data is a corporate asset, a common heritage, and only regulatory aspects should justify local ownership. In other cases, this ownership quickly becomes an obstacle to documentation: the corporate culture must favor making data available to the greatest number, under the responsibility of an entity or individuals.

If you would like to discuss the obstacles to the democratization of data described in this article, or if you would like a presentation of the Actian Data Intelligence Platform solutions for data-driven companies contact us.

About Anthony Corbeaux