Data as an Asset

The Cost of Not Knowing and Managing Data

Many of the organizations we encounter are still struggling to move from undisciplined and reactive data management to proactive and governed. As a result, a significant amount of time (up to 80%) associated with Report Generation and the Analytics process is spent gathering, correcting, and massaging data. Beyond the Information Technology (IT) department having a generated list of physical databases and other data stores, there is often no documentation of the business definition of what the data asset is at a summary level let alone a data element level. So many companies continue down the path of creating new data stores, replicating existing ones, and never archiving or decommissioning them under a governance process that is based on business value.

What continues to drive this pattern is the daily and weekly demand for more data to satisfy the information requests coming in, combined with the relative low cost of storage. But with the growing demand for higher quality, integrated data that is delivered in faster response cycles, organizations are beginning to realize their data management function must change. Organizations that don’t address the need for proactive data management risk not providing accurate timely information, and employee burnout as resources are overtaxed to respond to increasing demand for more data.

Treating Data as a true Asset

As companies rely more and more on data for business insight it must be treated like other assets within the business such as human capital, equipment, etc. As such, business information about the asset must be registered, evaluated, and continually managed. A key difference from the past as described above, is that the business must co-lead the data management effort in collaboration with IT. What has been missing is the business description and knowledge being well documented along with a centralized governance process to control the data assets.

How effectively are you managing data?

The following diagram depicts the continuum of data management maturity and how well an organization is managing data as an asset:


In order to drive up the value (lower cost, fast delivery, and accurate data) and reduce risks (slow business reaction and making decisions using bad data) the business must proactively work with IT to define and take responsibility for managing data. Data and the business context in which it operates needs to be captured and managed as a body of knowledge in a way that is viewable and understandable to the business.

Case Study Example: Healthcare

The Healthcare Industry is undergoing a major transformation with the implementation of the Affordable Care Act (ACA) and the pressure to lower cost by shifting to value-based care. In order for organizations to succeed in this transformation, collaboration among stakeholders (i.e. clinicians and business leaders) is imperative along with leveraging data to measure cost and value. Data must be recognized and treated as an enterprise asset for organizations to succeed in this transformed market place.

One of our recent Healthcare Provider Clients completed an implementation of an enterprise Electronic Medical Record (EMR) System. There was a business expectation that data would be better integrated for reporting and analytics once the system went live. Although data is being captured in an integrated system, only 7 years of history could be loaded into the new EMR, and the data must still be exported into a model for reporting outside of the EMR. In addition, over 50+ systems needed to be decommissioned and the historical data in these systems must be archived for both legal medical requirements and future clinical patient lookup.

Our analysis of the current environment (People, Process, and Technology) revealed the following challenges:

  • Data from many systems existed in undocumented “silos”.
  • IT was responding to an ever increasing demand for more reports from the EMR reporting Database.
  • There was no Data Governance in place.
  • No Data Architecture Strategy existed.
  • The Business was beginning to engage outside vendors to get the analytics they needed and IT was being asked to provide data extracts adding to an already high workload.

STS developed an Information Management (IM) Strategy and Roadmap for the client to unify their Data Architecture and launch a Data Governance council and related processes. In addition, a Business Intelligence Center of Competence (BICC) model was recommended that will evolve over time to enable self-service of data and reduce the IT workload as demand for information increases.

Case Study Example: Banking

In the Banking industry there are several drivers that have increased the need for more information and faster cycle times for producing and running analytics. Strong Tower is working with a regional bank to develop a joint business and IT roadmap (set of inter-related initiatives and solutions) for their Commercial and Retail Risk Management Groups.

The key drivers and challenges in the Risk Management area are:

External Pressure – Federal Regulators are pressing for more in-depth data management processes and analytics for operations and acquisitions. As a result Risk Management has a need to score each account with an AML score, perform CCAR and Basel analysis, etc.
Internal Pressure – There is increasing desire to create risk based pricing, perform analysis of acquired data or purchased portfolios, and free-up the time associated with data acquisition for skilled modelers by automating manual processes.

Improving Data Management

At the heart of one of the bank’s challenges is the significant time spent preparing (identifying, cleaning, and integrating) data for model execution. As a result, one of the key initiatives identified is an effort to significantly improve Data Management in the Risk Analytics Area. This effort will leverage the bank’s current efforts around Enterprise Data Management which is includes company wide, common data governance processes, and tools.

The data in scope for the Risk Management organization will be jointly defined and documented based on a standard data model for Banking, as well as processes and tools associated with this effort. The bank will also identify meta-data and associated rules thru the delivery of the models and analytics that the Risk Teams utilize.

Both IT and the Risk Management organizations will need to work as one team to define the sources, definitions, rules, and processing with the goal of reducing the work associated with data preparation by the business.

For the Bank, successfully addressing the Data Management problem will result in reduced time and resources in data sourcing and preparation, resulting in faster and more accurate model development and execution.

Realizing the Need for Better Data Management

If you are responsible for maturing the governance, quality or compliance of your data, here are some key questions about your organization to help gauge where data management improvement is needed:

  • Are business organizations and IT spending excessive (more than 30% of the total) time sourcing and preparing data for analytics or reporting?
  • Do you have a register of data assets and related statistics?
  • Is there an organization/tool to find out what data is available, usage, quality etc.?
  • Does each project start from scratch in defining, clarifying, and validating its data requirements?
  • Does the business have an integrated view of its critical data attributes and where they are used?
  • Who is tracking the level of data duplication and fragmentation across the organization?

To learn more on how Strong Tower can help you move your Data Management Competency forward, contact us today!