An effective data management tech stack is essential to the health and success of any business, regardless of industry. Today, data is generated at faster rates and higher volumes than ever before, so businesses need systems that are unified and agile in order to keep up. As businesses try to keep up with the surge in data volume, now is the time to reassess if their stacks are up for the job.

The way a business builds up its data management stack has deep implications across the business. Data constantly needs to be shared, be it from sales teams to customer service teams, or user data being shared with application developer teams. If data is the lifeblood of business, then an effective tech stack is the backbone.

Here, we’ll look at factors to consider when modernizing data management software, including the steps enterprises should take before deciding on a solution, common challenges to adoption of new systems and best practices for implementing new technology into the stack.

What to Consider Ahead of an Upgrade

Modernizing the tech stack can help an enterprise deliver more effective results for customers, cut costs, and increase operational agility. In order to deliver on the potential of a modern-day tech stack however, there are several considerations that businesses need to undertake first.

This includes taking an audit of which systems are performing at a level that can keep pace with today’s innovation, which are performing only adequately, and which are underperforming. Understanding where efficiencies can be created is essential, as enterprise data management needs vary from business to business. Taking the time to understand the most important aspects as they relate to your business goals can elevate your data management modernization strategies to the next level.

To kickstart the modernization process, businesses must first assess their data warehouse needs and the types of datasets that are going to be processed. This involves business leaders asking themselves how well their current data management plan is functioning, and if it can integrate data internally and externally to paint 360-degree views of customers. A working understanding of how the current system is functioning helps identify the areas that can be improved from an operational agility perspective and for the future.

When assessing how future-ready the current IT architecture is, businesses must also consider how well it’s set up for future innovation. This includes being agile enough to collect data from edge devices, IoT devices, sensors, and other connected systems being used in smart enterprises. Additionally, if the current IT infrastructure can process data from these sources, will it be available in real time for instant analysis?

As datasets and IT tools continue growing in complexity, businesses are often running multiple data processing systems and tools alongside each other. As they assess data management strategies, business leaders need to ask themselves if their systems are able to run concurrently with one another, and if that concurrence will exist even with increasingly complex datasets.

A future-proof data management tech stack will give enterprises the clearest picture of their business and potential for future innovation. In order to be “future-proof”, business must prepare for and overcome certain obstacles.

Challenges to Modernization

Data storage and processing is one of the largest pieces of the enterprise pie, and as such, takes up a lot of IT resources. When businesses identify a part of their data management stack to upgrade, they should be advised that there are some size-related hurdles that they’ll need to clear first.

Though many data management solutions nowadays are cloud-based, enterprises do use a mix of on-premises and cloud solutions. This means that upgrades to a data processing system can sometimes include onsite setup and maintenance costs associated with the data storage center.

Additionally, if a business opts to set up a hybrid mix of cloud and on-premises for storage, there will be a need for data migration and integration. Based on the size of data being shared from one storage site to the other, this can take some time. This is also where issues of data quality can be discovered, which will require the data to either be scrubbed for use or discarded as unusable.

Finally, another main challenge associated with modernization of data management systems is the compute and storage capacity. As mentioned, data management systems are oftentimes the largest IT element in a business, and it’s critical to have storage that can handle the datasets. This includes having enough power and physical space for on-premises storage sites. For data stored in the cloud, businesses should prepare for costs to vary based on data volume.

Upgrading a data management tech stack doesn’t have to be a daunting task. Preparing today for tomorrow’s innovation involves taking an honest look at how well prepared your stack is for the future, and if it can still support future business growth.

There are plenty of choices today for enterprises looking to improve their data management strategies as businesses leverage the cloud more and more. See how cloud-based services like the Actian Data Platform can take your data game to the next level, no matter what the future holds: www.actian.com/data-platform

About Traci Curran

Traci Curran is Director of Product Marketing at Actian, focusing on the Actian Data Platform. With 20+ years in tech marketing, Traci has led launches at startups and established enterprises like CloudBolt Software. She specializes in communicating how digital transformation and cloud technologies drive competitive advantage. Traci's articles on the Actian blog demonstrate how to leverage the Data Platform for agile innovation. Explore her posts to accelerate your data initiatives.

Global data has been rapidly growing for more than a decade, snowballing into massive quantities of data available to businesses today. Data is aggregated everywhere on the Internet – through web searches, social media, work files and text messages – as well as via IoT devices and sensors. This has led to impressive market growth, and according to Statista, the global big data market is forecasted to increase to $103B by 2027, more than double its market size in 2018.

There is no end in sight for the accumulation and growth of big data. So, the question remains, are you prepared to face the future of big data? Let’s look at several key steps you can take to prepare yourself for big data management in the years to come.

Database Server Farms Provide Fast and Simple Data Management

Let’s face it, big data doesn’t all easily fit together in one computer. Setting up a cluster of small computers to handle workloads is complicated to configure and difficult to keep running. It can also introduce problems, such as sharing skew and workload management difficulties, creating a highly fragile and difficult ecosystem. Further, a large, singular dataset and a centralized data warehouse of everything is simply not productive or necessary.

Instead, consider working with a flexible database server farm for an environment that’s easy to set up, manage and change. A homogenous database server farm offers independent servers without cluster complexity, a single database setup and the ability to add or delete servers without affecting others. Vector Analytics Database’s high performance and easy administration without cluster complexities make it a great option for a database server farm. Vector requires little to no tuning, so virtually no individual setup is needed, and it allows users to introduce variations that are typically found in a heterogenous database server farm.

Integration Makes Data Usable and Agile

Data supports decision-making, and data quality impacts an organization’s ability to provide their products and services. Company data must be usable and agile, as well as protected and governed, but managing data becomes more complicated each year as quantities of data grow. Because of this, organizations must harness the power of data integration by collaboratively using people, suppliers, and technologies to make better use of disparate data sources and inform decision-making.

The key steps to follow in the big data preparation process include extracting data from various sources, properly loading data, and transforming data for analysis. Ensure that during this process, you are following data governance standards and keeping security processes in mind. Using big data integration will allow your company to run with high-performing teams sharing information, data, and knowledge to support business decisions and customer service.

A Modern Data Platform Offers High-Quality, Real-Time Data Insights

Businesses are facing another challenge when it comes to managing seemingly infinite amounts of data – their data warehouses are not designed for analyzing big data in real-time. Most data warehouses are designed for what companies needed several years ago, with on-premises infrastructure and fixed capacity and processing optimized for relational database schemas. Today, organizations need scalable cloud infrastructures and on-demand resource scaling. Traditional data warehouses cannot accomplish this with the speed that modern businesses require.

A modern data platform designed for high-efficiency processing can leverage high-performance vectorized data processing and manages big, unruly data. Actian Data Platform helps organizations manage and analyze huge quantities of data quickly and efficiently, giving users real-time insights to make business decisions in the moment. The Actian Data Platform’s unique capabilities outperform traditional data warehouse solutions, delivering significantly higher performance at a much lower cost.

Better Manage Big Data in the New Year

Looking for modern, scalable solutions to help you tackle your big data challenges in the new year? Visit Actian’s product overview page to find solutions to your data management and analytics needs.

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.

Today’s data managers face numerous data-related obstacles on a regular basis – a surge in the pace of digital transformation, datasets growing increasingly more complex, and the proliferation of data by volume, just to name a few. However, one issue that is quickly surging up the priority list is the protection of data within the enterprise.

Keeping enterprise data safe certainly isn’t a new idea or priority, but it’s a task that needs attention now more than ever, given how quickly the threat landscape is shifting. Gartner recently identified the expansion of attack surfaces or points of entry for attack, as the number one trend impacting cybersecurity today. This trend began when Covid-19 forced many global businesses to revert to remote and hybrid setups for work. The distributed workforce created a situation where cloud architectures were suddenly loaded with systems and data that were ripe for attack.

As cybercriminals continue to target new attack surfaces to steal or hold data ransom, it’s worth noting why a data protection strategy is so important, how to bolster existing strategies, and the benefits of taking a hybrid approach to data protection.

Today’s Cybersecurity Threats

Understanding how to protect your company’s data requires understanding the threat landscape. Data and IT security managers are under constant threat from attacks targeting data such as phishing, malware, ransomware, spearing, and other forms of snatch-and-grab tactics. Much like the evolving complexity of enterprise data, cybercriminals have adapted their attacks to keep up with the technological innovation around them.

As mentioned earlier, the widespread use of remote working throughout the pandemic initially created significant stress on cloud systems that enabled work from home. As these systems became overloaded with data and users, threat actors found it much easier to spread their attacks through vulnerability points. A report from Deloitte found that nearly 50% of individuals had fallen for a phishing scam while working from home during the pandemic. This comes as a direct result of businesses cobbling together cloud-based systems without standardizing security across them. Savvy cybercriminals can pinpoint these weaknesses and exploit them to steal data.

Just as technological innovation has grown in complexity over the years, so has the adaptability of cybercriminals, who are evolving to keep pace with security professionals. The Deloitte report also found that during the pandemic, 35% of the attacks businesses faced they had never seen before – up from 20% prior to the pandemic.

Data breaches come with a steep price tag. Beyond the reputational damage of putting critical data in the wrong hands, leaving the door open for a data breach can cost businesses severely. A report from IBM found that data breaches cost businesses in the United States on average $9.44M in remedying the situation. The financial aspects of a breach cannot be overlooked and should be a top priority of IT and business leaders.

How to Stop Breaches in Their Tracks

Closing security gaps requires having a complete picture of your company’s data systems. Organizations need to map out their dataflows across the business and identify which systems and datasets are most mission-critical and need the most protection. This includes data that is both at-rest (sitting in data centers) and in transit (data flowing between two systems or data centers).

Once the most crucial datasets have been identified, then businesses need to understand which systems are accessing this data and ensure secure connections are made between systems. This is where work from home and remote working can complicate matters, as devices being used on home networks may be less secure than devices being used on corporate networks. A gap like that would require robust security protocols in place to ensure data being processed at home is not picked up by a malicious actor also connected to the network.

Finally, enterprises need to adopt a standardization model towards their security frameworks. The systems used to keep data safe while in transit or sitting in a database should all be following similar protocols to ensure there are no knowledge gaps among users that could lead to a vulnerability.

For many businesses, a hybrid integration strategy is the best approach to take for data protection and security.

Try a Hybrid Strategy

Many companies have a web of data and disparate data connectors throughout the business that need to be managed from a holistic ‘single pane of glass’ approach. This means having a central place to manage, monitor and update all data connections, giving direct visibility into the security of those connections. The best way to achieve this is by adopting a hybrid approach to data protection. This means having protection for all IT systems, whether they are on-premise, in the cloud, hosted with third-party services, or IoT devices.

Through the Actian Data Platform integration platform, businesses can easily manage all connections that exist within the enterprise, no matter where they reside. This visibility gives businesses the ability to manage all data flowing in and out of the business and can keep a more comprehensive eye out for potential security gaps. A hybrid integration platform like DataConnect can also help businesses regularly update credentials to systems that hold data, as well as more quickly respond to data breaches or other security issues.

Ready to up-level your data protection strategy? Read more about the Actian Data Platform.

About Traci Curran

Traci Curran is Director of Product Marketing at Actian, focusing on the Actian Data Platform. With 20+ years in tech marketing, Traci has led launches at startups and established enterprises like CloudBolt Software. She specializes in communicating how digital transformation and cloud technologies drive competitive advantage. Traci's articles on the Actian blog demonstrate how to leverage the Data Platform for agile innovation. Explore her posts to accelerate your data initiatives.

What is a Data Fabric, Anyway?

The data fabric has become a rising trend in technology over the last several years. Gartner defines a data fabric as:

“a design concept that serves as an integrated layer (fabric) of data and connecting processes. A data fabric utilizes continuous analytics over existing, discoverable, and inferenced metadata assets to support the design, deployment, and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms.”

A data fabric provides a consistent set of data services and capabilities across on-premises and cloud environments. It allows users to abstract data from logically and physically different systems into a common set of data objects, so you can treat them as a unified enterprise data set.

What is a Data Fabric Used For?

Data fabrics are not new; they originated in the mid-2000s when computing began expanding from data centers into the cloud. Over time, data fabrics increased in popularity as businesses embraced hybrid clouds. Lately, data fabrics have garnered significant attention as organizations move processing to multi-cloud environments and the edge. Companies are looking for a framework that can move and manage these new loads and securely integrate them into their systems.

Data fabrics are crucial in supporting digital transformation initiatives, as companies must leverage a variety of systems that may be spread across multiple clouds, on-premises, or remote deployments. Companies can share data across systems more efficiently by using a data fabric, resulting in better business insights and agility.

The data fabric design concept helps solve an age-old data problem – making things that are fundamentally different look and act similar enough to treat them as though they are the same. More significant challenges arise as analytic environments grow and develop, creating more urgency for the best possible solution.

Data fabrics provide a framework for teams to better handle complexities such as:

• Diverse data sources and types.
• Demand for real-time data for fast-paced decision making.
• Data siloes across business functions.
• IT systems spread across operating environments (on-premises, multi-cloud, mobile, etc.).
• Growth in operational analytics and business-led data modeling.

Discover More About Data Fabrics

If you’re working in a complex analytics environment and decision makers want real-time data, you should consider working with a data fabric. The Actian Data Platform offers users capabilities to implement a modern data fabric and access the data in real-time for informed business decision making. The platform’s built-in integration can also help you manage the flow of data across your organization. Learn more about how Actian can support your company’s data fabric journey by viewing the variety of data services we provide: https://www.actian.com/product-overview/.

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.