Big data is a rapidly evolving field. It offers tremendous opportunities for organizations of all sizes. So why are many top decision-makers skeptical of its benefits? One of the biggest answers is data fatigue.
Data fatigue is a growing problem. What is it and how can data scientists and decision-makers overcome it?
What is Data Fatigue?
Data fatigue is the reluctance or unwillingness to accept the benefits of big data. It tends to occur when key decision-makers have had a bad experience with previous big data projects.
Evgeny Popov, Senior Director for Lotame, wrote a great overview of the problem:
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“Every CMO I’ve spoken with have data or data strategy as number one on the priority list, while actually the data ecosystem growth velocity is not helping them to be effective in making right business decisions. There is an evident influx of new data vendors, which inevitably creates fatigue as marketers need to spend more time trying to wade through the fields and separate the wheat from the chaff. Even if they are successful with the latter, the volume of information and data points captured does not always translate to business outcomes. In fact, research shows that only 15% to 20% of data is “useful” and can push the bottom line.”
One of the biggest problems is that they don’t always see tangible results. It is also called performance lag between data accumulation and utilization. Since previous big data projects didn’t translate into an obvious improvement in ROI, many CEOs are agnostic or even hostile towards big data.
When Steven Maxwell, a partner with Newvantage Partners, pitched the CEO of a company, this is the response that he received:
“I’ll listen to what you learned from the survey as long as you don’t use those two words again — ‘Big Data’ — I’ve already told my team there will be hell to pay if one more person tells me, ‘we ought take a look at what Big Data can do for us,’ that may be the last suggestion they make at the company.”
What is driving their reluctance to get on board with the big data revolution? There are a number of explanations. The lag between harvesting big data and seeing a tangible return is often vast.
Here are some ways that data engineers must make sure every stakeholder is on board.
Have a realistic time frame for achieving measurable results
Big data is very valuable, but it does not provide immediate results. Executives, data engineers and project managers must agree on a reasonable time frame before they can begin seeing an impact.
Unfortunately, estimating the time frame is not going to be easy. Even if you have a background on similar projects, you must understand the nuances that can complicate your estimates.
Therefore, it is important to make a conservative estimate. If you worked on similar projects and found big data begin paying for itself after three months, you may need to estimate that it will take four or five months on a slightly more complex project. For simpler projects, you may want to stick to the estimate of three months from procurement to results. You may still run into unexpected obstacles that can cause a longer lag between collecting data and experiencing measurable results.
Develop Comprehensive End-to-End Funnel Solutions
Data itself is a valuable commodity. However, the decision makers that use it must understand its purpose.
Fragmented big data strategies are usually doomed to fail from the very beginning. The decision makers that rely on it must understand its core purpose in the funnel. This is why there is a growing shift towards end to end big data strategies.
If the same team collects and utilizes big data, the final will work much better. They will have a clearly outline strategy that focuses on collecting the right data from the beginning. They will understand how their data fits in with the long-term objectives.
This is most evident with more intricate marketing funnels, such as those involving email. Email marketers use new tools such as ZippySig, along with big data to optimize their marketing funnels. Data that is collected on customers during the initial stage of the funnel is used to optimize campaigns at the end. This only works if the team is on the same page and knows how data is structured and implemented.
Make sensible promises
Big data offers unique insights into challenges organizations face. However, it is not the infallible asset that many people make it out to be.
You need to be transparent if you want decision-makers to wait for the results of your big data strategy. They may be waiting for a few months to see results from your campaigns. Don’t disappoint them by making promises that you can’t backup.
