We’ve all been there before – your company decided on some new enterprise wide data and analytics platform; and now you are on the hook to implement it and ensure its success.
Gartner researchers estimate that over 80% of big data projects failed in 2017.
Minimizing the risk of your data and analytics strategy failing should absolutely be a part of the project plan. While failure may be common for some, there are ways to prevent that from happening to you. It’s all about your data and analytics mindset and focus; let’s examine.
You lack the right data questions to ask
Good questions are the foundation of data and analytics success. In fact, asking good questions is an underrated success skill. To get your thinking underway, brainstorm questions you can ask that impact strategic business decisions, enhance business performance or mitigate some type of risk for example. As you think about these questions, remember there are two types of questions to ask – ineffective and effective questions. Let’s explore some of these types of data analytics questions.
Ineffective Questions are typically too broad, vague or open-ended:
Effective Questions are usually specific in nature with a clear answer:
And while it is critical to ask the right questions, the quality of the data you are working with matters too.
You are analyzing garbage data and don’t even know it
Once you have landed on what types of questions you need answers to, you’ll need to aim those questions at worthwhile data. Our view? Assume your data is low quality until you can prove otherwise.
The first step in any data quality assessment lies in reviewing whether you have fallen victim to common data quality traps.
The three common causes of low-quality data:
Your data and analytics output is hard to consume
Who is the most critical consumer of your data analytics work? If you answered “my fellow data experts in the organization,” you’ve just missed the mark.
Too many data scientists and analysts suffer from a familiar expert’s curse – they speak to their peers. Instead, you need to connect your data to business outcomes and problems. For a bank, you might be concerned about fraud and return on investment. For a retail organization, you need to bridge the gap across the omnichannel customer experience – so capturing information that helps tell the story makes data and analytics that much friendlier to consume.
Use these best practices to redesign hard to consume data analytics reports:
Protip: You are the data and business intelligence expert but don’t let the expertise blind you to what information your audience needs and be sure to make your reports and presentations easy to consume.
You are confusing descriptive and predictive data analytics
Yes, this is a beginner mistake, but it is a crucial failure point to cover. You and your team may be swimming in data and statistics all day, but your audience may not know the difference. How do you decide where to focus your time and attention? We recommend starting with descriptive data analytics.
Find out if you are failing in descriptive data analytics by using these tips:
Once you have improved your descriptive analytics, move up to predictive analytics.
Your data and analytics tools are not up to the job
How do you know if your data and analytics tools and software are holding you back?
Check your situation by using the following questions:
Your Take Away
It’s easy to get excited with the new, shiny object, i.e. your newest data and analytics platform. However, you want to set yourself up for success, so before you get too caught up in the hype and promise, be aware of these five data and analytics failures. It’s better to be prepared and make sure your data management plans include analytics success strategies, rather than find yourself defending the results of
a failed implementation.
of executives say that having a strong master data management program is important to ensuring their future success.
of executives believe their organizations are underinvesting in their enterprise-wide data strategy.
of executives say their organizations rely on more than 6 data types that are essential to business operations.
of respondents who have employed MDM say their organizations' approach to MDM is moderately or very effective.
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