5 Mistakes Companies Make with Data Driven Decisions
Meet the Personas
A Non-believer of Data-Driven Decisions
Susie works for a large banking institution. She is frustrated, and terrified, because she was unable to mitigate the risk of a large investment gone south. Susie decided not based on data but based on her experience in the industry to make this decision.
Too Much of a Data Manipulation Guru
Then there is Charles; he works for a marketing agency. Charles used analytic visualization and data manipulation to back his data-driven decision for a gaming app client. He increased push notifications because it’s what the data told him to do. But Charles didn’t take account that subscribers might not like this. He is frustrated, and terrified, because he lost 50% of client’s notification subscribers.
To remain competitive in today’s business market, relying solely on gut instinct is no longer enough. Technology and data are present in every aspect of a business, so it only makes sense data leads the decision process today. The process of data manipulation and visualization allow companies to test theories, stay agile, learn from past mistakes and produce success. Data is extremely important, but so is human interpretation. A balance between technology and experience is necessary to make data-informed decisions for your organization.
Susie and Charles can teach us a lot. Here are five common mistakes companies make when it comes to data-driven decisions.
What are data-driven decisions?
Using data manipulation and analytic visualization to draw conclusions and ultimately make decisions based on patterns seen in the correlated data. Big data analytics turn numbers into actionable insights.
The Blind Spot in Data
Just like Charles, companies can become “too” data-driven. Some organizations don’t consider human decisioning. For Charles’ case, he used analytic visualization to see a pattern in the data. He saw the gaming app had a significant amount of push notification subscribers, and those notifications were causing consumers to open the app. He made a data-driven decision to increase the number of notifications daily, thinking this would increase users per day. However, consumers found this annoying and turned off push notifications and deleted the app. Charles should have considered the effects and asked, “how would this make me feel?” He also could have A/B tested a small group. It is important to take off your blinders and consider human feeling in data-driven decisions.
Before technology took a front seat in business, experience, tradition and gut feelings were the leading factors in decision making. In today’s world, tradition can be a growth and a decision-making hindrance. In Susie’s case as an experienced executive, she has a lot of valuable experience. That is important to draw from, but don’t forget about analytic visualization and data manipulation. They are your biggest asset in making data driven decisions.
(data) Garbage in, (data) Garbage Out
Some companies can see the advantages for data-driven decisions and analytic visualization right away. This is very positive. However, some companies ignore the importance of strategic planning in a big data project and the importance of data quality. Too much, happens way too fast. To create a strategic plan, start with asking what goals your organization is looking to achieve. Next, get accurate and quality data. If you don’t receive great data in the beginning, your decision outcome will be just as bleak.
I am going to give you another scenario. Jessica works for a large utility company. Upper management is pushing to adapt a new big data analytics technology to motor utility usage. To speed along the process Jessica cuts corners to get the data. Jessica also does not come up with a strategic plan for the data she receives. She has a hard time drawing a conclusion from the data visualization. In the end, the data-driven decision she proposes isn’t accurate because data quality is low. Jessica also got too overwhelmed with the possibilities of what this tool could do, and couldn’t focus on one improvement at a time.
We have that? Yes, you do have that.
Once companies can identify the goal(s) they are working towards, the next step is identifying the data you have and what you need to make accurate data-driven decisions. Another common mistake companies make is not looking internally to see what they can already do or what they already have. Don’t spend time and money on external resources if what you need is available without the hoopla. If it is immense, and the talent on your team can’t break it down, hire experts. It is all about achieving success with data-driven decisions.
Only Seeing the Tip of the Iceberg, Scratch Below the Surface.
James works for a large insurance provider. He was asked to gather insight on consumer data. James focused on online digital content. He used analytic visualization to determine that he needed to create more content. Without digging any deeper, he decided to create more blog posts. James is horrified because that data-driven decision he drew didn’t help with online engagement. Just like James, companies don’t always focus on the bottom line. It is important to scratch below the surface to understand the full meaning of the data. If James continued to manipulate the data, he could see his demographic prefers whitepapers and case studies over company blogs. You need all the data to make a data-driven decision.
While data-driven decisions are important, it is also imperative to consider outside factors before making information actionable insights. The industry is moving away from the term data-driven and more towards data-informed. This is where ethosIQ comes in. ethosIQ is a data collection and analytics solution provider that helps companies get a unified view of their disparate, siloed data. We provide the collection, visualization, and expertise to guide companies in making data-informed decisions. Dare to demo to discover the data-informed decisions that could lead your company to success.