August 18, 2022
10 minutes read
Information Insight

Why Data Management Matters

Look around at the IT assets that your company provides. You see your tangible assets like your laptop and cell phone. Then think beyond the assets that you can’t visually see, like your Slack app, Outlook email license, or malware protection. Now, take a step back and go deeper to think about all the processes those tools are powering, such as using your phone’s address book contact information to send an email to a client or DMing a colleague with a new sales lead. 

Data powers a lot of our IT, and our IT assets produce data as a byproduct of operations, and the natural conclusion is that it is an IT asset. But that conclusion is wrong – data is a business asset waiting to be turned into a product. And one that can generate real value. 

And like anything that can generate value, you need to understand the intention behind your company’s use for its data. Keep it secure. Use it properly. And then, it can be leveraged for greater gains and, sometimes, alternative uses.

At this point, most enterprises are sitting on the proverbial data oilfield with no means of accessing or utilizing those untapped resources. There’s a recognition that the oil is there, and all that is needed are some talented engineers to tap the reserve, and the value will start to flow.

But in reality, once that oil is out of the ground, it must be processed, refined, shipped, and matched with its appropriate use. It’s the same concept with data. Data is the asset, information is the product – and that product only generates value in the hands of the right consumer.

According to a NewVantage Partners survey of IT and business executives, many are not maximizing the business potential of their data. Only 39.7% of respondents said they’re managing data as a business asset, and just 26.5% said they’ve created a data-driven organization. 

Here we’ll examine why the management of your data matters, how you can overcome the challenges of achieving effective data management, and the consequences if you continue to treat your data as just another IT asset. 


Data management is the practice of collecting, organizing, protecting, storing, and maintaining the data created and collected by an organization, with the ultimate goal of making the information within the data accessible and useful to business operations. 

The concept of data management started back in the 1980s when the floppy disk was all the rage. It has since evolved with the acceleration of technology. We now have new kids on the data block with the Internet of Things (IoT), artificial intelligence (AI), virtual data warehouses, and cloud technologies. These innovative advances have enabled market participants to compete on analytics at lower costs, with fewer barriers to entry. 

At the same time, the digital revolution has made the world of data more complex and vastly larger as the world converts everything to software solutions. Most enterprises have committed to digital transformations, and a significant challenge associated with these programs is capturing the operational data and converting it for practical use within the analytics space. We call this data management. 

An acronym that is being used more frequently in the analytics space to refer to the key dimensions of analytical data is DATSIS. It stands for discoverable, addressable, trustworthy, self-describing, interoperable, and secure, meaning in order for a company’s data to be effective, reliable and valuable, it should be:

  • Discoverable – easily found and identified
  • Addressable – accessible and following nomenclature guidelines
  • Trustworthy – high quality and accurate
  • Self-describing – understood without the need to ask questions
  • Interoperable – multiple interfaces to search and find data
  • Secure – security standards and policies are regulated 

The term data governance is often grouped together with data management. Data management and data governance are partners in supporting an organization’s business decisions, actions, and goals. Although these two functions have some overlap, they each have different purposes. Data governance done right establishes business ownership over data assets, ensuring coherence across systems and departments and enabling contextual use. Data management codifies and enforces those policies and procedures to build quality right into the system. 


At Kenway Consulting, we’ve seen companies experience deep struggles with their data management, unable to bridge the gap between business needs and technical capabilities. Yet modern data platforms have made scalable and flexible an achievable goal. 

Kenway recently worked with an asset management company that needed a scalable and manageable data solution that would provide invaluable insights and analytics to better target its wealth advisor clients. The solution also needed to retain agility and flexibility to facilitate rapid prototyping and ad-hoc analysis. 

The asset management company was obtaining client data from multiple providers without a single source of truth. The data was scattered, outdated, and duplicated, resulting in difficulties garnering Customer-360 insights as well as poor business outcomes around its efforts for sales, marketing, and product distribution.

The asset management company’s data management challenges are all too common. According to research findings from SnapLogic, IT decision-makers report that, on average, 42% of data management processes that could be automated are currently being done manually, taking up valuable time and resources. As a result, almost all respondents (93%) believe improvements are needed in how they collect, manage, store, and analyze data.

These are some of the pain points that arise when a company has a disorganized and disjointed data management system:

  • Unknown risks which lead to reactionary risk management after the damage has been done from a potential threat.
  • Privacy issues around personally identifiable information (PII) and scalable processes to purge consumers’ data.
  • Lack of usefulness and endless hours spent wrangling data manually in Excel sheets from multiple sources to provide an accurate readout.
  • Quality issues and back and forth questions from data that is conflicting, inaccurate, and not timely.
  • Establishing trustworthiness of analytics teams and data platforms that enable a data-driven decision-making culture to flourish because accurate context is consistently provided and used across the organization.


An important point to remember is that data management extends beyond the technical aspects of an organization. Data management is far-reaching and touches on a company’s culture, mindset, and people. This is because the primary goal of data management is to benefit the organization and produce business outcomes. 

Data is a business asset that can be revenue-generating, revenue protecting, and help to decrease costs. Businesses can better achieve the results they want by looking at data management through multiple lenses, including the entire organization’s strategic goals to the more tactical perspectives of the data steward, the data author, and the analytics and reports consumer. Developing an understanding of the many personas in the enterprise analytics ecosystem is critical to driving data management that scales and creates compounding value.

The Q1 2022 Alation State of Data Culture Report examines the correlation between data culture and revenue. It uses metrics from the Data Culture Index, which measures an organization’s fitness to enable data-driven decision-making. Only 15% of companies qualified as a top-tier data culture, meaning widespread adoption of data search/discovery, data literacy, and data governance, versus 29% from Q3 2021.

When data mismanagement occurs, the effects can trickle down to all parts of an organization. It can cause a so-called “time suck effect” throughout the company, with people grappling with uncertainty around critical metrics, operational issues, or questions of meaning/definitions. By creating a culture driven by data, businesses can empower their people to realize the full potential of their data and properly manage it.


Going back to that asset management company example, Kenway addressed the client’s data management challenges around targeting and lead qualification. The data solution enabled the sales teams to eliminate manual data consolidation tasks and to spend that time leveraging insights from the now consistent and accurate data. Kenway helped to improve the organization’s strategic decisioning by making consistent, high-quality data accessible and shared across the entire organization.

Kenway has guided many companies across financial services, healthcare, telecommunications, and more in restructuring their data management systems for business success. Connect with us or browse our client success stories to learn how we can help you with your data management and make your data a viable business asset.

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