Data Governance

Data governance plays a key role in a meaningful analytics strategy. While the idea doesn’t spread feelings of excitement, its importance should not be overlooked or underestimated.

A Hare was making fun of the Tortoise one day for being so slow.

‘Do you ever get anywhere?’ he asked with a mocking laugh.

‘Yes,’ replied the Tortoise, ‘and I’ll get there sooner than you think.’

Aesop’s fable, The Hare & the Tortoise, seems timelier than ever for business operations in the digital world. We rush into using the latest and greatest technology, but we often forget to take a step back and make sure we are doing it the right way.

Understanding the Road to Meaningful Insights

Most industries use cost reduction and operational efficiency as key performance metrics. Like the hare in the age-old fable, however, businesses race to the finish line (operational efficiency) without fully understanding the impact or scope.

Leveraging visualization tools such as Tableau, Microsoft Power BI and QlikView have become industry standards for identifying operational efficiencies. However, the common misconception is that simply seeing your data will provide the long-term benefits companies expect. Taking the time to understand the road to meaningful data insights can lead to massive cost savings in building a long-term solution.

Reevaluate Priorities

IBM projects a 28% increase in demand for data scientists and data engineers by 2020. What remains a concern is that a significant number of organizations struggle to collect, classify, and clean their data. Companies should take the necessary steps to ensure that they aren’t just pumping out massive quantities of misaligned information.

We can all admit that the idea of data governance is unattractive. Checking and double-checking mass amounts of information, only to find it littered with human error is frustrating.

Data governance is, however, the most significant analytics strategy in business.

John Showalter, MD, Chief Health Information Officer at the University of Mississippi Medical Center states:

“We spent 18 months on governance before we produced any single analysis report. You want unsexy? All that time was spent on procedures and protocols with no analytics. I get this amazed look from people when I say we worked 18 months before generating a report”. Fourteen months later, “[John’s] team is productive,” he said. “We have produced 40 data visual apps and 1,200 reports with just five report writers. This is possible because they don’t have to ask any questions – they know the rules of the road.” [Healthcare IT News]

Don’t get me wrong, these tools can empower employees to ask and answer their own questions. However, companies must make it a priority to ensure the data driving these decisions is accurate, trusted, and secure.

Data Visualization

Where to Begin

For decades, people have exported and manipulated data from various platforms, such as SAP, Oracle, and Salesforce to give their superiors information for quick consumption. We now realize that moving data into ungoverned environments can compromise the overall analysis, as well as the trust of the consumer.

Stepping back to define your company’s overall analytics vision will better prepare you for success. Start by asking these questions:

  • Is our data environment ready to rapidly create immediate value?
  • Do we want a centralized or decentralized data structure?
  • What types of data will be used as part of the data and analytics systems (unstructured, semi-structured, and structured)?
  • In the current environment, how is raw data transformed once it has been ingested?
  • Are there teams in place today that can validate the data that is presented?

Answering these questions will help define your long-term vision. IT and the business can then collaborate on shifting toward a path of growth while focusing on security and governance. By building standard calculations inside your data sets and investing in authentication methods to filter confidential data, organizations can save time and money in the long-run.

Focus on Fundamentals

Seeing mass amounts of data consolidated for the first time is fun in the short-term. However, once these reports are in the hands of subject matter experts, the time spent building worthwhile analysis will be wasted if sales are split between “Company X” and “Company X”, due to a user’s careless typing.

During this time of rapid change, businesses rush to gain an edge on competitors. The timeless desire to act like a hare can be costly. Focus on fundamental steps to develop your data pipeline before releasing it to end users. This preparation will ultimately lead to the insights and cost savings for a long-term solution.

After all, “The race is not always to the swift.”


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