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Forum: Why your company is doing data and analytics wrong, and how to fix it

By Tom Pohlmann

Tom Pohlmann, head of values and strategy Mu SigmaIn today’s digital world, we have access to more data than we know what to do with. Businesses have come to gather as much information as they can and implement technologies such as business intelligence (BI) tools to analyze it. But are we really doing the best we can when it comes to putting the data to work for us?

As the head of values and strategy at Mu Sigma, a leading global provider of decision science and big data analytics solutions, I see a lot of businesses doing it wrong. That’s why we recently surveyed senior decision-makers across multiple industries in the U.S. to understand how companies are faring in respect to analytics.

The report highlights that a majority of senior leadership believe analytics is affecting company strategy and results in positive ways. Interestingly, companies that indicated they are underperforming against investors’ expectations tend to be more skeptical about the impact and benefit of data and analytics. Regardless of the attitudes around big data, the findings show that many companies are going about analytics the wrong way.

What we found is that many businesses are still misguidedly prioritizing data and technology needs over the need for better decision-making. The business world, along with the needs and demands of customers, is constantly changing and evolving. To stay relevant, organizations need to use data and analytics to address these changing needs, but because businesses aren’t paying enough attention to creative problem-solving, they are falling short in analytics.

Below, I address the key takeaways from the report, “The State of Analytics and Decision Science.

Why your company is doing data and analytics wrong, and how to fix it

Why your company is doing data and analytics wrong, and how to fix it

Poor data quality and lack of skills are top challenges. As more organizations begin to leverage data and analytics for decision-making, the demand for data scientists grows. But finding the right skill set can be challenging. While math, modeling and problem-solving skills are important, they’re not the only ones. Data scientists need to be able to understand what their analysis means for the company and communicate these findings to leadership. In fact, our research showed that more businesses are looking to improve their business acumen (44 percent) and communication skills (36 percent) than math (29 percent) or problem-solving (33 percent).

But it’s not just skill-set challenges that plague organizations looking to up their analytics game. In fact, 34 percent of respondents cited data challenges, rather than skills or lack of tech tools, as the top challenge they’re looking to address in the next 12 months. The deluge of data organizations gather can lead to poor quality, consistency and usability.

Starting with the end in mind. While having the data is important, knowing how it’s going to be used should be the main priority. Too often, organizations focus on the data, rather than prioritizing decisions, and our survey confirmed this mindset. An overwhelming majority of respondents (74 percent) indicated they lead with and sometimes limit themselves to the data they have available to them, rather than planning around the business outcomes they have in mind, when it comes to problem-solving. This could be in part due to limited bandwidth and resources dedicated to analytics.

However, with change from regulations, economic climates, customer expectations and advancing technologies affecting all industries, businesses need to start leading with their desired outcomes rather than with the data available.

Lack of structure leads to the Wild West of analytics. While some business functions like finance or IT are very structured, following strict processes, guidelines and procedures, the same cannot be said for analytics and problem-solving. Nearly 40 percent of respondents said they do not have a standard methodology for solving business problems.

This lack of structure can also be seen in the many different data ownership and governance models organizations adhere to. While at one place the CIO (23 percent of those surveyed) might be in charge of analytics, at another it could be the CFO (17 percent) or CMO (13 percent). What’s more, many organizations are now creating a role for a chief analytics officer (CAO) or chief data officer (CDO) to own analytics.

When it comes to governance models, it depends on the person responsible for the data and analytics. Most traditional organizations prefer a centralized model, where one designated group provides analytics services to the rest of the company. While more popular (44 percent) than other models, a centralized body of analytics is often seen as not fast or agile enough to serve organizations. The more progressive organizations with CAO or CDO titles tend to go for a federated model, combining the centralized and decentralized approaches with proper governance.

The good news? Organizations realize this lack of discipline regarding analytics isn’t working. In fact, 41 percent think that their ability to drive actionable insights out of their analytics work could really improve.

As we continue to unlock the value of data in business, figuring out how to use it to make better decisions will be critical. To view the full survey results, click here.

Tom Pohlmann is the head of values and strategy at Mu Sigma, a leading global provider of decision science and big data analytics solutions. In this role, he leads the company’s global brand and communications strategy, manages a portfolio of new client accounts and oversees the development of programs that continuously align the company’s work with its values and vision. Pohlmann has more than 25 years of experience in strategy and product development, messaging and branding, profits and loss accountability, mergers and acquisitions, customer insights and analytics, publishing and public speaking.

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