Information to Insight
Avoiding Data Nearsightedness
"What value can be extracted from our data?”
It’s a question clients have asked me many times. Whether valuating the results of a new product or reading end-of-quarter results, today data plays a starring role in just about every organization’s decision-making processes. Yet, as confident as data-driven decisions can feel, it can also throw a company off course if the data lacks integrity. Data should never trump the clarity of experience and professional judgement.
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- Data analysis with low integrity data can leave companies either pointing their strategy in the wrong direction or forever playing catch-up with competitors
- The deep value of data analytics comes from using data to access insights that have never been easily accessible in the past.
- It’s important not to silo your analytics team away from the operations and management units. An integrated, business-owner approach provides the broadest insights.
MNP is here to answer questions about the effective use of data at your organization.
The potential for this “data nearsightedness” problem has existed for the entirety of the big-data revolution. Nearly 10 years ago, a The Economist special report entitled “Data, data everywhere” helped introduce the term “big data” and cited how the likes of Facebook, Walmart and the Human Genome Project track billions of bits of inputs of information to glean patterns of behaviour that predict future paths. The article suggested there was little downside in the power of big data or utilizing data analytics — even with challenges such as storage needs and greater privacy concerns.
Today the infiltration of analytics in businesses big and small brings with it a host of important data integrity considerations. Data analysis with low integrity data can leave companies either pointing their strategy in the wrong direction or forever playing catch-up with competitors who are already reaping benefits from high-value analytic insight.
At the end of the day, proving hypotheses with data analytics is very useful, but deeper value comes from using data to access the insights that have never been easily accessible in the past.
Here are some guideposts to help you avoid data nearsightedness in your organization:
Businesses may fall into the pattern of allowing data to drive their mission and objective-setting process. This obscures what the fundamental goals of the business should be, and in particular, fails to focus on a clear idea of what the data should achieve in the first place.
Once a business determines goals for its data — whether it’s customer insights, better fraud protection, or business growth in new markets — it clarifies the type of data, technical expertise, analysis and reporting needed to achieve the best possible results.
Invest in Data Governance and Management
There is no more important consideration in analytics than ensuring the accuracy and consistency of data. Businesses without trusted enterprise data solutions may be operating with compromised data — whether through bugs, transfer errors or human error — or data that is limited in scope or inaccessible to those key stakeholders in the organization who need to view and analyze it.
It’s imperative to maintain a comprehensive data governance and management strategy with regular reviews of integrity protocols and at least one person on the team who can effectively explain all the consequences to even the least tech-savvy business leaders.
Creating high-value analytics requires expertise and technological mastery of advanced analytics — a plan on how to properly manage, deploy and evaluate data. But the team members with the right technical skill sets must also understand the goals of the business, from the process to the strategy to the risks and opportunities.
It’s important not to silo your analytics team away from the operations and management units that are core to the business. An integrated, business-owner approach embraced by all key stakeholders can broaden insights and establish alignment across the business.
Consider the Sources of Data
Any analytics-savvy team must also be proactive in identifying the optimal sources for data. A broad perspective is encouraged here: you may warehouse data using customer sales records or sensor tracking, track Facebook impressions and clicks on Twitter or assess the performance of your content-marketing blogs. Consumers also have a host of ways of interacting with a business, whether through email, digital tools, social media or bricks and mortar. All those interactions allow you to learn about their behaviour.
Businesses have to employ analytics expertise to weigh which of these interactions are most important. What’s more, your team’s own face-to-face interaction with clients, vendors and customers is irreplaceable as a source of extremely valuable information that can explain the reasoning behind the numbers. All of these inputs need to be weighed carefully and consistently.
At the end of the day, deeper customer insights, driven by the right breadth and uses of data, can allow you to tackle the key goals of a business with effective strategies. Whether it’s personalizing a campaign to reach a bigger audience or streamlining a process to drive higher customer conversion rates, just keep in mind that looking at numbers alone do not paint the complete picture.