How to Get Your Data Analytics Program off the Ground
The field of data analytics comes with a lot of promise. Data analytic tools and data analysts can take the information your business already has and transform it into insights that help enhance decision-making and provide clearer strategic direction
Businesses of all types and sizes are highly interested in what data analytics could do for their organization. We recently asked risk management conference attendees about the emerging developments they were monitoring, and 65% of the people we polled named data analytics as the top issue, ahead even of cybersecurity laws and industry trends.
If you are serious about putting a data analytics program in place for your organization, you should know there is some ground work to be done first. Below are a few steps to consider before you get too far down the path of deciding on and implementing a data analytics program.
Define the Use Case
The first and most important question your company will want to answer is: Why do you want to implement a data analytics program? There are so many applications. For example, Major League Baseball uses data analytics to “shift” their fielders based on a hitter’s tendency to place the ball in certain parts of the field. Retailers may be using data analytics to predict customer buying patterns and preferences. Accounts payable departments have had success employing data analytics into their processes to identify and address incorrect or duplicative vendor payments.
Management teams should get together to brainstorm their goals for a data analytics program. What are they looking to learn? Are they looking for insights in order to cut costs, quantify strategic direction, identify potential fraud, improve process efficiency, or all of the above? What data do they have, (orders, sales, workflow, etc.) and what data do they need to get? What publicly available data would provide value (benchmarking information, census data, and government compliance information)?
There may be “low hanging” fruit where a data set is already sitting there, ripe for analysis. For example, your accounts payable system may already have all of the information necessary to identify control exceptions and/or potential fraud. If data analytics is new for your organization, these quick wins may be just the sort of projects to which you should be applying data analytics to first.
You should also understand if there is data across your company that is difficult to consolidate, as that may indicate a business need for a data analytics solution. One of the appeals of a data analytics program is the ability to aggregate data from multiple sources in a meaningful way.
Understand Data Analytics Program Risks
Data analytics tools come with some risks. The biggest involves data security. In order to work as intended, data analytic tools will need lots of baseline data. They will also produce high volumes of data, and your organization should be prepared to address how it will use and distribute the data analytics results. Sensitive data should have limited access and users should be using secure channels to view it.
Data governance issues are also important, including the plan for managing data, how access to the data is controlled, and the team responsible for regularly reviewing or monitoring data access.
You also need individuals within your organization who can verify the quality of the data being fed to and produced by the data analytics tools. Is the data analytics team using the most up-to-date information? Is the information being used by the tool accurate? Is the data produced by the data analytics program accurate? These questions are critical for the success of your data analytics venture.
Similar to any other technology tool, change controls needs to be part of the data analytics risk management conversation. Organizations should have policies about who controls changes to the analytics tool and who manages the changes to the data architecture.
Your organization should also understand the information management resources you have on the personnel side. The exploratory conversation about what’s in place may reveal some early roadblocks or other pain points, such as key decision makers not having access to the data they need.
Start Thinking About Your Data Analytics Champion
Data analytics is an emerging and evolving technology solution. It may take a bit of a “sell” on behalf of data management enthusiasts to roll it out in your organization. As you move through the exploratory phase of data analytics, you may want to consider who on the team will serve as your data analytics champion.
The champion will help spearhead the process of moving the data analytics forward by getting consensus from decision makers about its use case. Your champion should be someone who is passionate about analytics who can also effectively communicate about the program to different (nontechnical) audiences.
Ballpark Your Costs
Part of the early conversation around data analytics needs to include your expected return on investment. How much does your organization want to invest in its program and how much do you expect to realize in return? Part of that will come down to the program’s strategic goals and objectives. Tools and resources come with varying price points. Do you want a data analytics program that produces enhanced reporting, or do you want tools that provide more sophisticated analysis that includes data science techniques? The latter requires more technical expertise than the former so, in addition to the tools, your analysts may come at a higher cost. It’s important to know the level of investment that will be needed early on in the process, as that might affect timing.
Put the Building Blocks Together
The exploratory phase is vital to the success of your future program. It can help gauge the interest from key decision makers and identify early obstacles (such as issues with current data architecture) that would affect future data analytics implementation.
For more information about putting data analytics into practice for your organization, please contact us.