Eliminating points of failure on your data team

This year is the year: you’re finally going to implement smart data processes into your organization and reap the benefits. You’ve overhauled how you’re collecting data, where you’re storing it and how it’ll be extracted into information. To get to this point, you’ve also likely invested considerable resources in your data personnel.

So, you’re all set, right? Not exactly.

Your heart is in the right place, but heart doesn’t automatically jibe with data. As Harvard Business Review stated in a 2013 piece titled, “You May Not Need Big Data After All,” a central reason why companies don’t see ROI from big data investments is because they don’t know how to manage and analyze their data let alone make business changes based on any insights discovered. The piece aptly states, “companies don’t magically develop those competencies just because they’ve invested in high-end analytics tools.”

In the four years since this piece was published, data has evolved considerably—namely, the implementation of artificial intelligence as a way to make information easier to digest and insights clearer to see. But many companies are still falling into the same trap of major systems and personnel investment without an internal culture that celebrates data and its potential in aiding everyday workflows.

The reasons for this are many, but often boil down to a lack of data transparency and too much burden on the data team to keep data processes moving. In other words, you have all your eggs in the data team’s basket. Here’s how to eliminate the points of failure on your data team so your organization can interact with data more efficiently.


Develop a Rigorous Data Dictionary

It’s well established that regardless of streamlined data warehousing methods and shiny data analytic tools, the companies that get value from their data are the companies that are able to communicate about data in a shared language. The data team you’ve already built-out should be able to dictate a comprehensive dictionary that includes basic data definitions, various metrics that are important in the organization as well as descriptions of those metrics that clarify potential misinterpretations. With a shared data language for all to communicate within, there’s less burden on the data team to spell out every initiative.


Document Data Ecosystem

As the data team develops a thorough but digestible data dictionary for the company to use, they should also document the entire data ecosystem. These professionals understand how the process flows in their own heads, but without the process clearly documented, what happens when outsiders have a question on something or a data team member leave and others come in?

Creating a data ecosystem document can also increase buy in from employees on using data in general. With a greater understanding of how data is collected, stored, cleansed, processed and analyzed, employees understand their role in making data work.


Make Data Accessible

Having multiple data sources is pretty unavoidable, but that doesn’t mean multiple data sources need to exist in the analyzation stage. Multiple data sets from different sources only makes data discussions more confusing and pushes employee sentiment away from wanting to be “data-driven.” When data from multiple sources is consolidated into one centralized view, everyone works off the same information and conversations are bogged down in unclear data and disparate opinions. This is a key area where machine learning in analytics is simplifying the process. By analyzing massive amounts of raw data from different sources and automatically pulling detecting trends, unusual patterns and data anomalies, companies can speed up data processes and free data analysts from tedious low-level (yet time-consuming) tasks like creating custom reports.

Engaging in the above initiatives won’t eliminate the points of failure within your data team overnight. But when points of failure are eliminated in data processes, companies can move faster and more decisively with their data.

As Brent Dykes writes in a piece for Forbes, “Most companies recognize data in the hands of a few data experts can be powerful, but data at the fingertips of many is what will be truly transformational

By developing a shared data language, giving employees a clearer understanding of the company’s data ecosystem and widening access to data itself, data teams have more bandwidth to focus on how data is aligning with higher-level organizational goals—after all, that’s the reason you’re paying those big salaries.

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