Actian Life

Telling a Great Data Story

Teresa Wingfield

November 23, 2022

people chatting in an office about data analytics and how to tell a great data story

What is a Data Story?

A story is a narrative, either true or fictitious, in prose or verse, designed to interest, amuse, or instruct the listener or reader. A data story can be like any story, but its narrative is focused on data insights designed to teach and motivate an audience to act.

Why Do You Need a Data Story?

In the world of data analytics, soft skills such as leadership and effective communication are just as important as hard technical skills. This is particularly true now that there’s increasing pressure to connect business value with cost in today’s economically challenged markets. Calculating the Return on Investment (ROI) for your data platform is very hard, and few can do this successfully. Thus, a data story gives you an easier way to communicate business value.

How Do You Tell a Great Data Story?

There are many things to keep in mind when creating a compelling data story. Here are my four recommendations to help you begin your data story development:

Tell the Right Data Story

A good data story includes something that is relevant or significant to your audience. The context of how data helps them do their jobs better is important. Data engineers might want more robust data pipelines and faster performance. Data scientists are most often concerned about having the right data, algorithms and data quality. Frontline workers have needs that vary across their business specialties. For example, a marketing user might want to be able to adjust a campaign more quickly with real-time insights into demand while a financial analyst might want to develop a better revenue forecast based on this same information.

Make Your Data Story Interesting

No one wants to hear the same old story. Creating a good data story means revealing something that will capture the interest of your audience. Sometimes it would inspire them to take action based on the message they are hearing. What are the ways that your data story can help uncover a new opportunity or provide a fresh insight into how to solve a challenge? Think about the stories that have stood out to you. Were there any that inspired you to think about things differently? To research and learn more about something? To do something? When possible, include how data insights from real-time analytics can help improve specific metrics or meet goals that your audience is responsible for.

Show and Tell

Just sharing the story isn’t enough. How can you present your data story in a way that engages your audience in a way that they can take something away with them? There’s a saying: a picture is worth a thousand words. This rings true when you are trying to convey an idea quickly and effectively. So be sure to use the visualizations that are available in your data analytics tool in conjunction with your own representations.

Plan for a Sequel

Like an enduring story that we remember for a long time – we sometimes anticipate what’s going to happen next. The data story shouldn’t just end after your presentation. You need the data story to continue by highlighting what actions you want your audience to take after hearing it. You can suggest ways to turn insights into action, or better yet, ask your audience about their ideas and follow up after your meeting to gauge the impact your data story has had on the business.

How Can I Strengthen My Data Story?

The best data stories are built around real-time data analytics. Improve your data story with the Actian Data Platform with real-time insights in a single solution for data integration, data management and data analytics.

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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 Management

Boosting CX Through IoT and Edge Computing

Actian Corporation

November 21, 2022

hands holding a cellphone that exemplifies the relationship between customer experience and managing data in the cloud

While not a new concept, edge computing is gaining steam, with more businesses embracing this technology at a breakneck pace. A recent Gartner report found that in 2022 alone, enterprises will create and process over 50% of the data they generate at the edge, outside the traditional data center or cloud infrastructure. This number will only continue to climb over the next few years.

With a surge in edge computing comes a revolution in the way businesses collect, process and store data. This revolution will open new doors to improvements in customer experience (CX) and transform the way businesses and customers interact in the future.

Working at the edge will introduce new challenges for businesses, especially as the devices that operate at the edge continue to grow in scale and complexity. Common issues include security concerns and understanding how to use edge data to connect with users in a non-intrusive way.

Edge Computing 101

To effectively drive business results through edge computing, we first need to conceptually understand what it is and how it fits into an enterprise. Edge computing refers to the distributed framework that seeks to put applications as close to data sources as possible. These sources can include internet of things (IoT) devices such as smart watches, robotics, warehouse sensors, and more.

The benefit for businesses leveraging edge computing is that they can process data near devices that generate data. Businesses can process locally generated data faster to get real-time insights and that helps them react quickly to changing conditions. The edge promises to deliver a more immersive and interactive CX. The amount of data these devices generate can be massive. Edge computing analytics allow businesses to sift through high volumes of data, keeping only what is useful.

Customers have more control and options over where and how they spend their money, especially in the online shopping environment where it’s easy to find options and compare prices. Edge computing gives brands the opportunity to instantly interact with customers across multiple channels to provide highly personalized experiences that encourage purchases.

Real-time data collection allows companies to dynamically adjust promotions, providing the right offers to the right customers – at the right time. For example, sensors within a clothing store can detect where a shopper is located, tie in historical shopping data about them, and then suggest deals on items that shoppers may be interested in buying. This real-time CX is hyper-personal to an audience of one and establishes a relevant shopping experience for the customer.

The power of the edge, though able to provide tangible benefits to organizations looking to improve CX, comes with its share of complexities.

Challenges at the Edge

Securing data at the edge and data sourced through IoT devices is a major challenge. As businesses increase the number of IoT devices used to generate and collect customer data, their attack surface area grows. Each new device, be it a sensor, smart thermostat, or connected speaker, opens new doors for cyberattacks.

The tricky part is ensuring the security of each device, as it often lacks the computational capacity for built-in security. In addition, edge devices are often misconfigured or are left with default configurations that are not secure. Threat actors can exploit security vulnerabilities to spread malware and steal data.

When it comes to creating a better CX through edge and IoT, businesses need to carefully toe the line between being helpful and being overbearing. For example, a grocery store leveraging sensors and a connected shopping cart may be able to deliver special sales to shoppers in the store but run the risk of bombarding them with too many notifications. This can come off as intrusive and leave customers feeling as though they’re being followed. When done properly, however, it can yield remarkable results and create experiences that keep customers coming back for more.

CX at the Edge

Given the ever-present nature of IoT devices and their continued adoption in the enterprise, there are opportunities at the edge to elevate CX strategies to their next level. Operating at the edge helps businesses democratize and curate experiences, as it cuts down barriers between customers and the brands they interact with. Being more functional at the edge and operating closer to customers means that the experience they receive is the one that is best suited to them.

Additionally, given the expansion of 5G networks and innovations in Wi-Fi technology, edge computing latency is dramatically dropping, enabling brands to deliver experiences to end-users even quicker. The edge gives IoT applications a localized basis for processing and storing data, which businesses can analyze through a local network. With latency reduction, experience optimization can happen even faster.

At the edge, brands can generate data that builds real-time insights that improve and enhance the CX and improve business intelligence in data stored in the cloud.

Want to learn how edge data management for IoT and mobile can help your organization modernize its edge application data processing and analytics? Go to the Actian website for more information.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.

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Actian Life

Becoming an Emotionally Intelligent Leader

Emma McGrattan

November 18, 2022

hands on top of the others symbolizing the positive results in a team

Leaders across every organization learned valuable lessons about leading their teams more thoughtfully during the pandemic. Here at Actian, we learned that being a flexible, adaptable, and empathetic leader goes a long way in building trust with our teams. The pandemic and resulting seismic shift in how we interact with our colleagues shed light on the importance of understanding how external factors can impact mental health. Today, the onus is on leadership to foster an emotionally intelligent and empathetic working environment to make employees comfortable and feel supported in their roles.

A vital trait for today’s leaders, and the key to truly supporting your team, is having a developed sense of emotional intelligence. Leaders, across every industry, need to be able to foster an environment that allows employees the freedom to cope with the emotions and stressors in their lives while juggling work responsibilities.

Emotional intelligence, also known as emotional quotient, is generally defined as the ability to identify one’s emotions, apply them to problem-solving situations, and manage them positively. Personally, I equate being an emotionally intelligent leader with being able to make genuine connections with my team members. These “soft skills” are becoming more important than ever to company success (especially in a distributed workforce), as they allow us to become better, more empathetic, and more approachable leaders.

Of course, emotional intelligence comes more naturally to some than to others. Thankfully, it is a skill that can be learned and nurtured throughout a leader’s career and lifetime. Let’s look at a few traits of emotionally intelligent leaders and how one can develop these skills.

Emotionally Intelligent Leaders Clearly Express Employee Value

Leaders can acknowledge employee value by showing that they trust team members to complete their work well, without constant touchpoints. Make a point to eliminate micromanagement, and instead, delegate tasks, training employees in new skills where necessary. At Actian, our employees have flexible schedules allowed by the benefit of remote work. People want to arrange their day in a way that is cohesive with their personal life – whether it’s taking their kids to school, joining an after-work club or hobby, or simply prioritizing family. Offering flexibility like this is one way to confirm that leaders trust employees and value their contributions.

A key soft skill that emotionally intelligent leaders express is empathy. An empathetic leader will understand that any number of unseen factors could delay a project – sickness in the family, relationship problems, or kids struggling in school can all impact an employee’s work performance. An emotionally intelligent leader will have situational awareness and encourage open, honest, and ongoing communication. Keeping an “open door policy” helps employees feel empowered to reach out when they are struggling and demonstrates that leaders care about the team members and not just about their contributions to the team.

Emotionally Intelligent Leaders Strengthen Connections with Employees

People with high emotional intelligence are skilled at creating strong, lasting connections with others. One of the best ways to improve connections with employees is to schedule time to connect one-on-one. Tech leaders are often busy, but packed schedules are the enemy of progress.

Additionally, when employees do have personal or professional issues they might like to discuss, they will feel significantly more comfortable initiating the conversation with a manager they already have an established relationship with. Leaders who put in the effort to establish those connections from the beginning will be better equipped to help their employees find solutions to these problems.

Emotionally Intelligent Leaders Let Their Own Walls Down

Becoming an emotionally intelligent leader can be challenging. To help overcome any personal reservations, leaders should first work to develop a sense of self-awareness. It’s crucial to deliberately identify, understand, and regulate your own emotions before you can support others. Improving self-awareness allows you to expand your emotional scope to better understand and support employees.

Another way to connect more deeply with employees is to find and create teachable moments. Draw from your own mistakes and successes to develop an environment that focuses on continuous improvement, rather than perfection. Encouraging learning will in turn improve employee confidence, comfort, and overall satisfaction.

Emotionally Intelligent Leaders are Authentic in Their Approach

Last, but certainly not least, emotionally intelligent leaders will come to work as their authentic selves. This will lead employees to do the same and make regular wellbeing touchpoints easier. Bring your undivided attention to check-ins and show a real interest in your team members. A people leader must display that they care about their employees, not just about solving the problem at hand.

Lessons learned during the pandemic and the subsequent move to remote work will equip leaders with the skills needed to overcome future high-stress moments. Leaders must think about their workers holistically, looking for early signs of stress and other mental health factors. Health, especially mental health, is fragile, and once it’s fractured it is hard to put it back together again.

Interested in joining a company that values its employees’ contributions and prioritizes the wellbeing of workers? Take a look at the open positions on our Careers page to see where you could fit in!

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About Emma McGrattan

Emma McGrattan is CTO at Actian, leading global R&D in high-performance analytics, data management, and integration. With over two decades at Actian, Emma holds multiple patents in data technologies and has been instrumental in driving innovation for mission-critical applications. She is a recognized authority, frequently speaking at industry conferences like Strata Data, and she's published technical papers on modern analytics. In her Actian blog posts, Emma tackles performance optimization, hybrid cloud architectures, and advanced analytics strategies. Explore her top articles to unlock data-driven success.

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Data Integration

Customer Experience and Cloud Data Integration, Explained

Actian Corporation

November 16, 2022

A network of people icons connected by lines illustrating the relationship between customer experience and cloud data integration

With a myriad of customer experience (CX) tools available today, enterprises are flooded with options when it comes to the services they use to interact with customers. The challenge for businesses is sifting through the many data silos that these tools create. While CX tools are helpful for efficiency, they can create organizational blind spots if data is not integrated effectively.

Data integration is pivotal to the success of any CX program, regardless of the technological solutions that have been deployed. Enterprises managing data in the cloud need to know where that data lives, how to integrate it into their technology stacks and how to leverage it for analysis. Data management tools that automate the integration process are eminently more effective than manual integration and can yield more accurate views of where customers sit on their lifecycles.

Recent reports found that nearly 60% of corporate data globally is stored in the cloud. As the volume of cloud data storage continues to increase, it’s worth understanding the critical role data integration plays in CX, and how organizations can improve their integration strategies to yield better business results.

Data Integration Hurdles

One area where businesses tend to struggle with data integration is understanding the details of where their data is stored and how it’s processed. Oftentimes, data generated by different business units can be disparate. A very simple example of this is date formats. In the US, the date format is MM/DD/YYYY. In many other countries, they enter the day first and then the month – DD/MM/YYYY. If a global retailer wants to send a birthday promotion to a customer, they need to know which format was used to capture a customer’s date of birth.

Another common plague of poor data integration is data duplication. Multiple customer records can lead to poor customer service. For example, if different systems are not connected, it may be difficult to access relevant customer information. Customer service representatives may have trouble identifying the products a customer has purchased, as well as their serial numbers and other relevant information. This can lead to delays in service and unhappy customers.

A lack of access and visibility to customer data can leave businesses flailing when it comes to CX efforts. Today’s consumers have extremely high service expectations and demand personalized experiences. Without easy access to data, CX teams have no way to tailor an experience and gain the desired outcome.

Fitting Data Integration Into CX

Overcoming data integration hurdles can be challenging for businesses but is a necessary step to move the needle forward on the quality of CX efforts. Businesses need to have a holistic understanding of who their customers are and can only do so when their data is effectively integrated across the enterprise. This can be difficult when data is exploding in volume and complexity. New datasets that are continuously generated require a more thoughtful approach as to how they’re managed, analyzed and stored.

Businesses need a full understanding of their data, who’s using it, and for what purpose. They should ask themselves the following questions: What do I need to deliver on CX goals? Where did this data come from, and how was it generated? Is this data compliant with privacy laws and regulations? Is this data easily integrated for use across the business? Once there are clear answers to these questions, only then can the integration process begin.

Undergoing data integration in a way that can benefit CX efforts requires several steps to be successful. Data professionals need to be able to easily integrate and transform data so it can be stored securely and used effectively to improve customer experiences.

This approach to data integration gives businesses a deeper understanding of their customers, their lifetime value, where they are on their buying journey, and results in more competitive insights. Data integration also enables organizations to protect against customer churn and improve products and services. Serving as a single platform for integration, transformation and storage that combines first and third-party data, the Actian Data Platform provides businesses with real-time insights and access to hundreds of data sources and does so without performance disruptions.

Nailing CX through efficient data integration should be a priority for every business looking to improve their relationships with their customers. Actian’s ease of integration through automation simplifies this for businesses and helps optimize their decision-making processes on next steps with customers. To learn more about how Actian can help your business unlock maximum CX insights at a fraction of the cost of other leading providers, visit our Actian Data Platform webpage.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.

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Data Analytics

The Best Ways to Deliver on the Promises of Real-Time Data Analytics

Actian Corporation

November 14, 2022

Image with binary code in green and blue that represents real-time data analytics

I summarized some of the key capabilities that businesses should look for to meet the demands of real-time analytics. I’d like to expand upon these key questions that may directly impact the success of your organization.

How Do We Scale and Manage Data and Analytics Across the Enterprise?

Many have mistakenly thought that once they moved their analytics to the cloud, they would have infinite scale. After all, you just add more resources such as additional nodes and instances as you need them. You simply sign up for a solution’s free trial; everything is working great, and you move forward with your purchase. But after several months, requirements to add more data, a lot more data, continuously materialize, and you find out there’s still a long way to go. Take these predictions into account:

  • International Data Corporation (IDC) predicts that the global data volume will expand to 175 zettabytes by 2025.
  • Forbes predicts that 150 trillion gigabytes of real-time data will need analysis by 2025.

Not only are you overwhelmed by the volume of data, but you also find that it’s hard to reuse data pipelines for data ingestion and to ensure compliance as data becomes more widely used. Now, you realize your real-time data analytics initiative is “stuck”.

You didn’t go wrong with starting small. Starting small is often the best approach if you have a plan to scale and manage your data and analytics as your needs grow. Look for solutions that are flexible, agile, and scalable to meet your key performance indications and service level agreements. You should also be able to readily share data across use cases and provide consistent management, governance, and compliance.

How Do We Source, Manage, and Deliver Data That is Timely, Relevant, and Trusted, to the Right Customers?

We want near real-time data, empowering “next best action” decisions in the moment. This means that data must be present to front-line workers in a manner that is actionable, in the context of their job. The best real-time analytics is embedded within the environment its customers operate in.

So, what does embedded analytics mean? According to Gartner:

“Embedded analytics is a digital workplace capability where data analysis occurs within a user’s natural workflow, without the need to toggle to another application. Moreover, embedded analytics tends to be narrowly deployed around specific processes such as marketing campaign optimization, sales lead conversions, inventory demand planning and financial budgeting.”

Include embeddable analytics in your capabilities to make sure your users are getting the maximum value from data. Often this involves embedding analytics within business applications your users already leverage to do their jobs with enhancements to inform their next best actions. Embeddable analytics can also involve alerting and recommendations if specific thresholds are met, or if specific anomalies are detected, sent to tools your users already have. In short, your front-line users often don’t need to learn completely new technologies or applications or learn how to become a data analyst to benefit from advanced analytics.

How Do We Democratize Data While Protecting Privacy, Complying with Regulations, and Ensuring Ethical Use?

Data privacy regulations are expanding across the globe. Gartner says that 65% of the world’s population will have its personal data covered under modern privacy regulations. Companies are becoming increasingly concerned about significant risk exposure. For instance, violators of General Data Protection Regulation (GDPR) may be fined up to €20 million, or up to 4% of the annual worldwide turnover of the preceding financial year, whichever is greater.

However, simple encryption is not enough to ensure compliant real-time data analytics. For analytics to be useful for various use cases in your organization, different data attributes need to be visible to different authorized users, while being de-identified or redacted for others. For example, the sensitive data your customer-facing users need to see will vary by geography, product line, business function, and identity of the customer.

Your data analytics software will need to provide new data security techniques such as column-level and record-level protection, and dynamic masking, to ensure authorized users see only what they need to see to do their jobs, nothing more and nothing less. Be particularly vigilant when evaluating cloud-native databases and analytics tools since many don’t support these techniques.

How Do We Change the Culture, Empower Employees, and Hire and Retain Data Talent That Makes All This Possible?

Your culture matters just as much as your data analytics software. A good start includes defining three things:

  • Your purpose for the platform (why it exists).
  • Your vision for the platform (what it hopes to deliver).
  • Your mission (how will it achieve the vision).

Building collaboration and trust between data owners, engineers, data and business analysts, and other stakeholders paves the way to ensure you can offer the right data to the right decision makers at the right time without leaning on IT.

Learn More

Learn more on business imperatives driving real-time data analytics, key capabilities you’ll need to be successful and an overview of how a data analytics software solution maximizes business value.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.

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Data Analytics

Customer Experience (CX) Challenges for Small Businesses

Teresa Wingfield

November 11, 2022

a digital projection showing the outcome of customer experience challenges

Customer experience (CX) is top-of-mind for business leaders who are weighing how to best make decisions that encourage customer loyalty and engagement. There has been a strong push across all industries to improve the quality of experience customers have, which in turn, drives revenue growth.

Successful deployment of CX initiatives comes with challenges, however. While CX challenges exist for enterprises of all sizes, small and medium businesses (SMBs), or organizations with under 1,000 employees, face their own unique set of hurdles. Among the economic strains felt during the COVID-19 pandemic, SMBs have felt the sting the most, with over a third reporting closures throughout the past few years.

Challenges such as staffing, smaller technology stacks, and pandemic-borne issues such as supply chain delays have put SMBs in the unenviable position of maintaining customer loyalty, at a time when resources are strained, and loyalty is at a premium. SMBs are trying to maintain their current customer base when consumers are more willing to try out other brands and shopping experiences than ever before.

Improving CX is no short order for SMBs. Here, we’ll share some of their top challenges and offer tips and insights on how to address them.

Small Businesses Hit the Ground Running

In part due to their size, SMBs have a tougher CX battle than larger organizations that have more technological resources and bigger customer bases. Large organizations enjoy access to bigger swaths of data to accurately portray images of who their customers are, and how to reach them in their lifecycle.

For SMBs, this means that serious CX issues can arise when data is incomplete or not up to date. While lower-quality data can present issues for businesses of any size, SMBs particularly feel the pain when they’re unable to weave disparate data points together for a complete 360-degree view of their customers. A large enterprise will typically have backlogs of historical data that it can easily stitch together to help determine what a buyer’s next steps might be and when and how to engage.

Further, as SMBs continue to grow and scale in size, so too must their technology stacks that analyze data. SMBs need to assess if current systems within their stack are setup to intelligently bring data together to inform customer behavior. Oftentimes, SMBs that are focused on growth are operating with legacy technology. Outdated, legacy technology can hold back CX efforts, and SMBs don’t always have the same wide-reaching budgets that larger businesses have to replace old systems.

Overcoming SMB Challenges

CX is a huge priority for SMB leaders. A recent report by SurveyMonkey found that over 70% of SMB business owners identified this as their number one priority for growth. For this growth to happen, however, SMBs must be prepared to manage and analyze data.

The same report also found that data analysis and acting on data are among the top challenges SMBs face with CX. When determining CX goals, SMB leaders need to take stock of the current systems and ask themselves these three questions:

  • Does our system offer a holistic understanding of our customers?
  • Can it provide a snapshot view of where customers currently are?
  • How do we take that view and use it to deliver superior experiences for customers?

By reflecting on this, SMBs can assess if their systems can meet customer needs and effectively reach micro-segmented audiences. From there, SMBs can draw up a CX plan and put a plan of attack in place.

The Actian Data Platform offers SMBs the ability to take data within their stacks and seamlessly connect it, ensuring they can make informed, meaningful decisions. For SMBs strapped with low IT support, the Actian Data Platform is easy to deploy and use, offering a single solution for data integration, management, and analytics. This solution keeps costs low, while simplifying data sharing across the business.

SMBs often face challenges that larger organizations do not need to worry about. But those issues should not get in the way of creating a great CX. With thoughtful analysis and a low-cost solution in place, SMBs can overcome challenges integrating data needed for CX and create a loyal customer base.

Want to learn how Actian and the Actian Data Platform can help your organization take its CX strategy to the next level? Learn more!

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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 Management

Management and Governance: A Successful Data Strategy

Teresa Wingfield

November 9, 2022

man holding a tablet with data showing a data governance dashboard

It’s no secret that businesses are using their abundance of data to generate insights that will advance their position in the marketplace or to make strategic decisions that will enhance value for their customers. Businesses that get the most out of their data aren’t those that happen to collect the most data, but those that do the best job of controlling the data they collect.

Many mechanisms define the act of data control. The two most talked about – and most easily confused – are data management and data governance.

Some organizations consider data governance as a component of data management. Others give governance a higher rank, suggesting that data management carries out governance’s policies. So, what’s the truth? The truth is that the model works both ways. Data governance and data management are two separate terms that cover different functions. They also work together to ensure that enterprises make the best possible use of their data.

What is Data Management? The term is best described as the management of all architectures, policies, and procedures that serve the full data lifecycle needs of an organization. It is an IT practice that aims to make sure that data is accessible, reliable, and useful for individuals and the organization. The term can also refer to broader IT and business practices that enable the use of data in the most strategic way possible.

Key aspects of data management include processes connected to data preparation, the data pipeline, and the data architecture. It’s critical to prepare data to make it usable for analysis. The pipeline pulls the data from various sources and loads it into storage options, such as a data warehouse, data lake, or cloud data platform. The data architecture defines the formal flow of data across its lifecycle.

Where Does Data Governance Fit In? While data management focuses on practices, data governance is about rules. The rules determine the appropriate use, handling, and storage of data. Data stewards set guidelines for who owns which data sets internally and who is authorized to access, edit, and circulate them. Governance rules also spell out how organizations secure data and comply with ever-increasing government regulations.

A well-designed data governance program includes several teams – usually one that oversees governance, a governing committee, and data stewards. They set standards and procedures for important matters ranging from data quality to data stewardship to data transparency.

The differences are clear: data governance charts out a broad set of policies implemented across an organization, and data management puts those policies – along with other best practices implemented along the way – into action. Data management is the execution, and data governance is the roadmap that guides the execution.

How Data Management and Data Governance Work Together

Looking at these terms another way, they complement each other like components in a landscaping project. Data governance scopes out the project – how it is going to look, what materials to use, and who is on the project team. Data management carries out the work. You could build a house without a blueprint, but the possibilities of making mistakes and overseeing critical aspects of building a home will run high. As a result, it will take much longer to build at great expense. Data management and data governance work together to maintain and protect data through a blend of processes and policies. The following are examples of the two concepts in action:

Your data governance policy could require that organizations keep customer data on-site for 10 years to meet regulatory requirements. Implementing data management processes can ensure that data is archived and deleted in a systematic manner.

Data management and data governance can also ensure proper data access. If a data governance policy dictates that only employees who need personally identifiable information (PII) to perform their jobs can access it, a data management process can grant role-based access to employees with appropriate authorization.

Organizations need to optimize their use of data to take advantage of digital transformation initiatives, machine learning, artificial intelligence, and other emerging technologies and practices. Creating sound data management and data governance practices provides control over data assets that will go a long way toward contributing to future success.

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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 Analytics

Four Ways Your Analytics Journey Can Help With Customer Behaviors

Actian Corporation

November 7, 2022

depiction of reducing customer churn using predictive analytics

We all thought, or at least hoped, that the COVID-19 pandemic would be history by now. Conditions are certainly better, but the end isn’t in sight. We still don’t know if COVID-19 will evolve into an endemic disease that we can more easily live with or if there’s a more deadly mutation just around the corner.

COVID-19 and its impact on consumers continues to ebb and flow regionally. Customer expectations and how they want to interact with businesses constantly evolve, with market dynamics influenced by the virus and its downstream economic and social impacts. For instance, during the pandemic, customers prioritized a safe shopping experience, often moving their shopping online, over saving money. But now, customers are once again trying to save money as consumer prices continue to rise due to pandemic-imposed supply chain shocks. They’re also increasing their visits to physical stores now that vaccines are available to help protect them from infection.

What Does This Mean for the Future of Data Analytics?

The pandemic has made navigating the right analytics journey essential to the survival and recovery of your business. Here are a few key examples:

Make Real-Time Decisions

In the “new normal” of pandemic-related health risks and restrictions, customer behaviors can change in real-time due to COVID-19 risks as well as supply chain impacts. Organizations thrive when they can use real-time analytics to monitor and react to changes as they occur in real-time. Those who cannot may struggle. A good customer journey depends on a good analytics journey. Organizations can attract and retain customers more effectively with relevant data available at the right time to see customer behavior changes and market impacts as they happen. It can also inform them of the next best actions in the moment.

Use Analytics in Digital Innovation

The International Data Corporation (IDC) says that enterprises now plan to spend more than half of their IT budgets on digital innovation because of the pandemic. Digital innovation intersects with analytics to optimize critical business processes such as buying processes, customer experiences, personalization and delivering new business models.

Predict Customer Behaviors

Your analytics journey ought to consider ways to accelerate Machine Learning (ML) and Artificial Intelligence (AI) to improve how you anticipate and understand customer needs and preferences of customers. Imagine being able to predict behaviors such as what customers want to buy, where they want to buy it, how much they are willing to pay, and the method they want to use for payment.

Make Your Supply Chain Resilient

Businesses need real-time analytics that will help them reinvent their supply chains to adjust to new realities quickly and efficiently. Analytics can give you visibility into demand volatility, supply shortages, inventory stock challenges, transportation bottlenecks, warehouse, and labor inefficiencies and more. This provides the transparency you will require for a fast-moving and optimized supply chain.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.

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Data Analytics

Actian Data Platform Helps Deliver Real-Time Data Analytics

Actian Corporation

November 4, 2022

Cloud data warehouse representation of Actian Avalanche

Organizations need to put relevant, trustworthy, and actionable data directly into the hands of their front-line workers and decision-makers, in a manner that improves situational awareness, as change is happening. This empowers users to decide on the best courses of action in the moment. Here’s how you can use our Actian Data Platform (formerly Avalanche) to maximize the business value of your data.

How can the Actian Data Platform Help Your Business?

The Actian Data Platform provides a trusted, flexible, and easy-to-use data platform for real-time data analytics. This highly scalable platform can be deployed in any cloud, on-premises, and in hybrid and multi-cloud environments. With built-in data integration, businesses can quickly build pipelines to ingest and transform data from any source, providing accurate, complete, and timely data into the native data warehouse and/or other targets. Businesses grow revenue and improve customer experience by bringing together data from enterprise systems, third-party data sources, and SaaS applications.

Finally, data management is also built-in, enabling organizations to run operational and transactional workloads at scale and to meet enterprise service level agreements (SLAs) for scaling, availability, and usage monitoring. Together, these capabilities empower data consumers to be truly self-service in standing up their analytics solutions, in a single platform with common design-time and runtime experiences.

Delivering Today While Building for the Future

Actian Data Platform makes data easy so that businesses can connect, manage, and analyze their data to make the most informed, meaningful decisions. This data platform is designed to be the most trusted, flexible, and easy-to-use platform on the market. Here are ways it helps deliver on the promise of real-time data analytics:

Accelerated Data Modernization

Quickly ingest data into the platform with a single user interface for self-service integration, analytics, and data management. This enables anyone to be a data practitioner and helps build a data driven culture organization-wide.

Superior Price-Performance

Built to maximize resource utilization delivering unmatched performance and an unbeatable total cost of ownership.

REAL Real-Time

Patented technology allows real-time updates of a data set without impacting query performance and costs. This allows data consumers to analyze always up-to-date data, thus they are confident they are responding to current reality. This is critical when unpredictable changes impact customers, suppliers, and employees in real-time.

Single Platform

One solution for data integration, data management and data analytics lowers risk, cost, and complexity, while allowing easier sharing and reuse across projects than cobbling together point solutions.

Flexibility, Deploy Anywhere

Any cloud, hybrid, and on-premises – plus it is API-driven to embed analytics within applications and systems, so that relevant data is delivered in context.

Role-Based Security Policies

Reduce the time and effort to comply with data and privacy regulations without compromising the usefulness of data to intended consumers.

Accelerate Your Business With Real-Time Data Analytics

Learn how leading companies across industries use the Actian Data Platform to maximize business value.

Learn how a single platform for data analytics, integration and management can accelerate your analytics use cases.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.

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Data Intelligence

What is Data Modeling?

Actian Corporation

November 3, 2022

Data modelling

Data modeling is a central step of software engineering. A data-driven company’s objectives are to evaluate all data dependencies, to explain how the data will be used by the software, and to define the data objects that will be stored in the database for later use. Are you wondering about what data modeling is, its founding principles, and the different types of models? Follow this guide:

The life cycle of data, while it may seem technically complex, is conceptually quite simple. First, you need to collect the data. Then you need to clean and organize it. Finally, you need to understand how you can use it. This crucial phase is based on data modeling. The idea is to create a visual representation of an entire data portfolio (or certain segments of the data) to easily identify the different types of data available, the relationships that may exist between these different types of data, and how they can be grouped, split up, or in any case organized to interact and generate value.

Data modeling, therefore, plays a key role in knowing how to exploit your data. Data models are built to meet the needs of the business. So, while there are different types of data models, one should never lose sight of the company’s objectives for data modeling to be truly effective.

Some of the advantages of data modeling include: reducing the risk of error during database software development, saving valuable time during the design and creation of databases, and ensuring consistency in the design of data systems. Data modeling also promises to simplify the communication between data and business teams.

The Different Types of Data Modeling

To get started on the path to data modeling, you need to start by knowing the main types of data models. Very schematically, there are three types of models:

The Conceptual Data Model

The conceptual data model gives context and helps teams understand the data outside of the technical dimension. The conceptual model is for everyone in your company, even those who lack technical skills. The conceptual model describes the data contained by the system, its attributes and data constraints, the business rules that govern the data, and the data security and integrity requirements.

The Logical Data Model

Logical models deliver more detail about the concepts and relationships in a data domain. In other words, they describe entities and attributes to provide a clear representation of the purpose of data for the business. A logical data model is a model that is not specific to a database. It describes the data in as much detail as possible, regardless of how it will be physically implemented in the database. Characteristics of a logical data model include all entities and the relationships between them, the attributes of each entity, and the primary key of each entity, for example.

The Physical Data Model

The physical data model represents how the model will be built in the database. A physical database model displays the entire table structures, including the column name, column data type, column constraints, primary key, foreign key, and relationships between tables. A physical data model will be used by database administrators to estimate the size of database systems and to perform capacity planning.

How Data Modeling Works

Data modeling is based on three key models: the relational model, the hierarchical model, and the entity-association model. The relational model is both the oldest and the most commonly used. It deals primarily with numerical data and is used mainly in mathematical calculations such as sums or averages. There is also the option to move towards a hierarchical model, which is optimized for online queries and data warehouse tools. In this case, the data is classified hierarchically, in a descending structure. Finally, there is the E-R model, which is used to generate a relational database in which each entry represents an entity and has fields that contain attributes.

Guarantee the integrity of your data, make the use of your data assets more reliable, and facilitate the development of a data culture within your company. Data modeling will allow you to be part of a virtuous circle of data use.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.
Data Intelligence

What is the Difference Between Data Fabric and Data Mesh?

Actian Corporation

November 3, 2022

At first, organizations were focused on collecting their enterprise data. Now, the challenge is to leverage knowledge out of the data to bring intelligent insights for better decision-making. Numerous technologies and solutions promise to make the most of your data. Among them, we find Data Fabric and Data Mesh. While these concepts may seem similar, there are fundamental differences between these two approaches. Here are some explanations.

It is no secret that the immense volumes of data collected each day have many benefits for organizations. It can bring valuable customer insights so companies can personalize their offers and differentiate themselves from their competitors, for example. However, the growing number of digital uses creates an abundance of information that can be hard to exploit without a solid data structure.

According to Gartner’s forecasts, by 2024, more than 25% of data management solution vendors will provide full data structure support through a combination of their own and partner products, compared to less than 5% today.

In this context, several avenues can be explored, but two stand out the most: Data Fabric and Data Mesh.

What is a Data Fabric?

The concept of a Data Fabric was introduced by Gartner back in 2019. The renowned institute describes a Data Fabric as the combined use of multiple existing technologies to enable metadata-driven implementation and augmented design.

In other words, a Data Fabric is an environment in which data and metadata are continuously analyzed for continuous enrichment and optimal value. But beware! A Data Fabric is not a finished product or solution – It is a scalable environment that relies on the combination of different solutions or applications that interact with each other to refine the data.

A Data Fabric relies on APIs and “No Code” technologies that allow synergies to be created between various applications and services. These solutions thus enable the data to be transformed to extract the quintessence of knowledge throughout its life cycle.

What is Data Mesh

The concept of Data Mesh was introduced by Zhamak Dehghani of Thoughtworks in 2018. It is a new approach to data architecture, a new mode of organization, based on meshing data. Data Mesh is based on the creation of a multi-domain data structure. Data is mapped, identified, and reorganized according to its use, its target, or its potential exploitation. Data Mesh is based on these fundamental principles: the data owner, self-service, and interoperability. These three principles enable the creation of decentralized data management. The advantage? Bringing about interactions between different disparate data domains to generate ever more intelligence.

The Key Differences Between Data Fabric and Data Mesh

To fully understand the differences between Data Fabric and Data Mesh, let’s start by discussing what brings them together. In both cases, there is no such thing as a “ready-to-use” solution.

Where a Data Fabric is based on an ecosystem of various data software solutions, Data Mesh is a way of organizing and governing data. In the case of Data Mesh, data is stored in a decentralized manner in their respective domains. Each node has local storage and computing power, and no single point of control is required for operation.

With a Data Fabric, on the other hand, data access is centralized with clusters of high-speed servers for networking and high-performance resource sharing. There are also differences in terms of data architecture. For example, Data Mesh introduces an organizational perspective, independent of specific technologies. Its architecture follows a domain-centric design and product-centric thinking.

Although they have different rationales, Data Mesh and Data Fabric serve the same company objectives of making the most of your data assets. In this sense, despite their differences, they should not be considered opposites but rather complementary.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.
Data Analytics

Don’t Rely on Outdated Advice for Digital Transformation

Teresa Wingfield

November 2, 2022

Digitally illustrated binary data tunnel to represent digital transformation

The road to digital transformation is not straightforward and has not rapidly accelerated over the past few years. As businesses modernize systems and processes to keep pace with technology innovation, they’re looking for insights and road markers to help guide them along their journey.

When seeking advice, business leaders often turn to industry peers for insights into their digital transformation efforts. This type of knowledge sharing helps leadership teams stay abreast of industry and market shifts, and how best to respond to them. However, given how quickly digital transformation moves today, coupled with an unpredictable market, leaders should consider carefully where they source their often outdated tips and tricks.

Referencing older digital transformation models not only hinders innovation in the enterprise, but it can also lead businesses to make poor strategic decisions that cut revenue and customer loyalty. We’ll share some advice that once held merit, which leaders should largely avoid in today’s fast-paced, digital-first world.

“Transform everything, and stop at nothing”

During the automation boom of the early 2010s, businesses deployed automation to many systems and processes with little regard for how over-automation might create inefficiencies. This is akin to the ‘shiny new toy’ effect when a new idea or innovation is announced; everyone wants what’s new even though they don’t know yet how it effectively fits into their systems.

Digital transformation is no different. In the early days of digitization, IT teams and leaders felt that every single element of an enterprise needed transformation – and fast. Companies would often invest too broadly in top-down transformation models which would have sky-high goals and minimal results.

Businesses that do too much too fast often find themselves underwater with systems that aren’t set up or functioning properly. Businesses that want to digitally transform today should draw on lessons from businesses that are over-automated and focus on one system and process to improve at a time. By taking this route, enterprises can test individual elements of a new solution, discern how that fits into the current stack, and then move to the next system.

“Create separate IT functions for the old and the new”

Historically speaking, digital transformation efforts often involved splitting the IT team into two groups; one to manage the maintenance of legacy systems and another to help drive innovation with new solutions.

While a business may be tempted to have separate, dedicated teams to perform these functions, they create division and silos. The team that’s tasked with maintaining legacy systems will be stuck working with technology that’s monolithic and outdated, and the other team will work with innovative new products. Working on new technologies and solutions helps IT professionals learn skills and understand how these systems will guide the future of the enterprise. Workers who focus on legacy technology will spend their time on systems that are fading out of favor, and this may make them feel left behind.

Rather than creating silos, companies should create IT teams that are agile and collaborative, with cross-functional groups that aren’t segmented by technology (new or old). This model means that all teams are trained on new technologies, while sunsetting legacy systems. This also allows for broader training on new processes, which democratizes the digital transformation process and rallies everyone to work together to accomplish the same goals.

“Build fast, measure later”

When new systems and technologies become available, businesses are often fast to adopt them, as outlined in the earlier ‘shiny new toy’ example. The same sentiment applies to IT teams rapidly building up solutions without measurable goals and outcomes.

It’s tempting to get a new solution up and running as fast as possible, but this method doesn’t allow for the necessary amount of time for successful adoption. Since digital transformation is a journey, not a destination, it would be a mistake to implement a solution before knowing how to measure and analyze its results. If an airplane quickly fueled up without assessing how much gas it needed to reach the destination, passengers may land earlier than expected. The same notion applies here. Brands must accurately assess if a new piece of technology will help achieve digital transformation goals. Foregoing this assessment can lead to undesirable outcomes and potentially stunted revenue growth.

What should businesses do then? They should begin the assessment process before building a new solution to gain a clear view of what they hope to measure, analyze, and achieve.

Want to learn how Actian can help your organization along its digital transformation journey, supported by data-driven insights? Learn more: https://www.actian.com/

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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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