In an attempt to bring you the best content within leading enterprise technology categories, Solutions Review editors scour the web on a daily basis for insights that can have real impact and help you to move the needle. We often share best practices that inform IT leaders on what to do when it comes to selecting and deploying BI and data analytics software. However, we’re going to take a different approach here.
A recent article in ITworld entitled 7 sure-fire ways to fail at data analytics got us thinking about what not to do when it comes to utilizing business analytics in an enterprise setting. Written by CIO contributor Bob Violino, the posting outlines 7 “traps that jeopardize or squander the true value of analytics.” We read the article, available here, and pulled out the three least obvious factors that you’ll want to avoid in order to ensure success with your next initiative.
1. Ignoring data quality
Data quality pertains to the overall utility of data inside an organization, and is an essential characteristic that determines whether data can be used in the decision-making process. There are a multitude of tools available that allow businesses to match, clean, correct, validate, and transform data so that it can be analyzed in a database, data warehouse, or analytics product.
Ensuring access to current and high quality data is the only way to make relevant analysis possible. Organizations need to constantly evolve their strategies to clean and add missing values to data so they can maintain updated datasets for future use. Data should also be self-describing enough to where folks beyond the scope of IT can not only have access to it, but understand what it means, where to apply it, and how it should be viewed.
2. Overlooking executive acceptance
One of the most complex situations in any organization is trying to convince management that a new piece of technology is needed. That can be a daunting process in and of itself, but once a buying decision is made, there can still be complications when deciding how to use a new product. In this way, avoidance in seeking executive acceptance for a new data analytics initiative can set the project off on the wrong foot.
Analytics needs to be paired with the organization’s business objectives before it can have any real, lasting impact. This is where collaboration with executive comes into play. If the analysis fails to meet specific business goals, or it requires too much in terms of rearranging existing protocols, then it becomes a waste of valuable resources.
3. Failing to take end-user skills into consideration
You could purchase the best business intelligence solution on the planet, sync up perfectly with executive management, and have clear goals to solve, but if end-user technical skills don’t add up, then the analytics project will fall flat. Managers need to take their available talent into consideration before selecting a new software product or embarking on an analytics initiative.
As the article points out, this can be a major problem when organizations are transitioning from simple analysis to more advanced and predictive tools. This is a common problem, and something more companies are dealing with on a daily basis. Given the extreme shortage of data scientists and advanced predictive skills among job candidates, matching analytics tools with objectives and end-user skills is a real challenge, but something that can cost you dearly if overlooked.
We encourage you to read the ITworld article in full.
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