The Data Intelligence Challenge: Next Generation Data Transformation for Your Enterprise

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As enterprises develop strategies to meet the challenges of a post-pandemic business landscape, data visibility, regulatory compliance, data literacy and data governance can all be delivered and enhanced with data intelligence.

 

Businesses understand that the masses of data they now collect is a precious asset that is often not fully exploited for its value and potential commercial opportunities.

Recent estimates from IDC state that in 2019 alone, 45 zettabytes of data were created. This massive quantity of information is expected to expand by a CAGR of 26% by 2024. Coupled with is a lack of data visibility. Indeed, a recent survey from Splunk revealed that over half (55%) of the data contained with a business is defined as ‘dark data’ that is unknown, uncaptured and critically unused.

As businesses re-draw their digital transformation roadmaps in the wake of COVID-19, how they manage data will be pivotal if they are to thrive in a post-pandemic business landscape.

Data intelligence is the key to unlocking the value of data, improving the efficiency and effectiveness of data-native workers, raising the level of trust that organizations have in their data, and offers practical solutions to tackle data governance that can become overwhelming without effective data intelligence tools.

One of the core components of a strong data management strategy is visibility. Often enterprises will have structured, unstructured, and siloed datasets that need to be identified and integrated to reveal the value they contain.

Having clear sight of these data sources is critical, but more importantly, also having a rich repository of metadata that enables businesses to fully understand where data is stored when at rest and how data moves through a business or organization as it is used and consumed is vital for all of today’s enterprises that want to remain leaders in their industries and sectors.

Businesses are increasingly migrating to the cloud. In practice this means having a hybrid mix of on-premises data and cloud data sources to manage. In this context, enterprise data visibility can be complex. Here the importance of data governance can’t be overstated. To ensure enterprises meet all their regulatory responsibilities, hybrid data environments demand that organizations connect data visibility and data governance into a strategic data management tool.

The business of data

The trinity of data visibility, governance, and empowerment are core drivers for all businesses and organizations. The 2021 State of Data Governance report revealed that: “84% of organizations agree that data represents the best opportunity to develop a competitive advantage during the next 12-24 months. These organizations recognize that if they do not continue to find new ways to use data to proactively customize products and offers for customers, they will likely be disrupted by competitors that figure it out before them.”

As data forms the foundation of all strategic initiatives, it is vital to understand that data is not just an exercise in IT enhancement or expansion. Instead, data visibility reveals the need for an integrated approach that meets the needs of every data stakeholder.

Enhanced data visibility and governance enables everyone that encounters each information source to intelligently protect and make use of the data they encounter. And data-driven business transformation (supported with data governance, centralized data cataloguing and data literacy efforts) unlocks the accessibility and value of this information at scale to drive more strategic decision making and improve operational efficiencies.

And shifting to a data-first business creates an efficient foundation for more data automation. The current State of Data Governance report showed that 93% of organizations have room to apply more automation to data operations. The application of Machine Learning will, over the short term, become transformative for many enterprises. And the evolution of moving towards a more active use of the metadata collected and catalogued by IT teams to drive DataOps processes by implementing metadata-driven automation will deliver significant gains.

Tools and control

The masses of data that every business contains and is constantly collecting has massive potential opportunities if this information can be used strategically. Raw data is useless. Information that can be leveraged for commercial insights must be discovered and centrally catalogued with each data artifact tagged with its corresponding metadata. Automation is the only way to efficiently tackle the task. The resulting repository of metadata, combined with business context and governance, can then be used as the one true source to reveal intelligence about all data across the organization’s information landscape.

This gives enterprises a strategic asset to speed the delivery of new data pipelines and to ensure the information business stakeholders will receive can be delivered with the contextual understanding and guidance to make it an operational advantage rather than a risk.

Automation makes metadata harvesting, management, activation and enrichment a practical reality. Especially as IT teams wrestle with the ever-growing, and ever-changing data landscape within their organizations.  Here, erwin Data Intelligence by Quest is the ideal platform.

Using advanced automation and augmented AI, enterprises can now more efficiently protect and see the value of their data.

A great example of erwin Data Intelligence in action is how E.ON uses the erwin Data Intelligence to gain greater governance over the massive quantities of data the company manages across 12 European countries.

E.ON’s Chief Data Officer, Juan Bernabé-Moreno, commented: “E.ON recognized the need to make the most of our data assets in shaping our products and services and understanding our customers. When we started digitalizing the company, we wanted to put a system of data governance in place to identify data assets, simplify documentation, and improve the quality of information throughout our organization.”

The result was E.ON identified more than €8 million in business impact after 18 months. They have connected more than 65 systems across four countries with 15 business units using erwin Data Intelligence for data governance. And E.ON estimates a potential savings of 30% on external data management costs and a 50% reduction in time spent on data discovery because better data availability and quality have a direct impact on productivity for each data-driven activity across the enterprise.

E.ON’s Bernabé-Moreno concluded: “Data governance is something that we must work on every day, and the [erwin Data Intelligence] platform is helping; our processes are working, and we are better able to meet the needs of our companies across Europe, each with different concerns.”

The power of adopting a metadata approach to information assets is the ability to then use automated data intelligence tools to enable detailed analysis of specific datasets and to visualize this information to support innovation and reduce risk.

Also, erwin Data Intelligence uses automation to help companies efficiently collect metadata from all data sources into one central location. Businesses can then activate the metadata collected to produce detailed data lineage, impact analysis and other visual and non-visual aids to see the data assets available throughout the organization and better understand them.

One company that has taken the data it has and converted this into a strategic advantage is the pet wellness company Petco.

Kiran Kanetkar, Senior Director of Data and Analysis, Petco explained: “Before we implemented erwin Data Intelligence by Quest, my team used to constantly get questions from many different business users about available data, which was taking a lot of time. By providing all of this data catalogue information in a self- service manner, our business users are empowered and able to perform their analysis much faster. And that has actually led to efficiency changes in our development process as well.”

The power of the erwin Data Intelligence tool is the self-service approach that enables non-technical users to access the centralized data resource to discover available data assets with business context, governance guidance and even data quality and crowdsource ratings to help them easily determine the best fitting asset for their needs.

One of the things that we liked about erwin was it had good visual representation of how data could be connected,” said one erwin user. “This helps users understand that, and the primary reason we were looking at erwin was really for data literacy across the organization.”

Data will drive business transformation post-pandemic. Therefore, your company must harness its information to reveal potential insights that could support commercial innovations.

Data-first approaches are now central to business development and innovation. The test of enterprises is to expand data visibility and combine data intelligence with data governance, to help their organizations maximize the value and use of their data. Doing so will produce a competitive advantage while at the same time, protect against data misuse and associated operational risks.

erwin Data Intelligence by Quest enables organizations to establish the enterprise data visibility, data governance and data literacy needed for roles and leaders throughout the organization to efficiently manage, understand, protect, and strategically leverage data.

To learn more about erwin Data Intelligence by Quest, visit www.erwin.com