<ul><li>There is an explosion of valuable digital data generated by consumers as we live our daily lives.</li><li>Advances in technology such as AI, Machine Learning and Cloud Computing provide numerous ways to leverage the data.</li><li>Apart from having Big Data, companies need to have an analytical mindset and culture, merging the best minds with technology.</li></ul>



Big Data

<p>Like any other discipline, becoming good at analyzing data requires seasoning and experience. Executives with deep analytical expertise, sitting on the top of the organizational charts are responsible for spreading a culture of evidence-based, data-driven decision making.</p>
<p>This also ensures that <b>data quality, data hygiene, implementation of data management and data privacy </b>are respected and adhered to.</p>


Data Governance by Top Executives

<p>The larger group of non-executives need to reinforce proper analytical techniques, and while not everyone can be an elite quantitative analyst, there is a certain basic level of proficiency that can be attained.</p>
<p>Employees need to be provided with project opportunities and made to attend training academies that provide the specific activities to be completed for increasing their analytical capability.</p>
<p><i>Organizations that invest in senior-level expertise and reinforce daily analytical activities will be able to take advantage of Big Data to expand and evolve in the future.</i></p>


Analytical Thinking Routines

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<h4>Executive Summary</h4>
<p>It's only when deep expertise exists at the top of the org charts that a penchant for evidence-based decision-making will develop pervasively throughout the organization. It's too common to devolve responsibility for analytics modeling to junior positions, which prevents analytical mindsets from really taking hold. Like any other discipline, becoming good at analytics requires seasoning and experience, which junior employees are unlikely to have. Essential areas of expertise include a deep understanding of the datasets being analyzed, and the ability to recognize their limitations and potential biases. An understanding of proper research design is a must, as are knowledge of basic measurement concepts such as reliability and validity. Without this in-depth knowledge of data and the appropriate application of analytical techniques, the wrong conclusions could be reached, and sub-optimal decisions and actions could be taken. Organizations that invest in senior-level expertise and reinforce daily analytical activities will be best positioned to take full advantage of the data and technology that abound today and will undoubtedly expand and evolve into the future.</p>
<p><figure> <img src="https://hbr.org/resources/images/article_assets/2020/02/Feb20_19_1166603537.jpg"><figcaption> Abstract Aerial Art/Getty Images </figcaption></figure></p>
<p>We're in the middle of an analytics revolution. The change is being driven by numerous factors, but two are more important than all of the rest. The first is an explosion in the amount of valuable digital data generated by workers and consumers as we go about our daily lives. The second is advances in technology, such as machine learning, artificial intelligence, and cloud-computing platforms that allow us to interpret and leverage these vast amounts of data.</p>
<p>Yet companies that excel with analytics have more than access to great data and technology. These companies recognize that success with analytics also requires an analytical mindset among its executives and an analytical culture in the business. In other words, it's critical to bring the people along with the technology. There are two easy steps firms can take to do this.</p>
<h3><strong>1) Ensure That Executives Have Strong Analytics Capabilities</strong></h3>
<p>It's only when deep expertise exists at the top of the org charts that a penchant for evidence-based decision-making will develop pervasively throughout the organization. It's too common to devolve responsibility for analytics modeling to junior positions, which prevents analytical mindsets from really taking hold. Like any other discipline, becoming good at analytics requires seasoning and experience, which junior employees are unlikely to have. Essential areas of expertise include a deep understanding of the data sets being analyzed, and the ability to recognize their limitations and potential biases. An understanding of proper research design is a must, as are knowledge of basic measurement concepts, such as reliability and validity. Without this in-depth knowledge of data and the appropriate application of analytical techniques, the wrong conclusions could be reached, and suboptimal decisions and actions could be taken.</p>
<p>Another essential function requiring executive-level direction is the implementation of proper data governance. Applying analytics at scale demands good data hygiene, from defining the architecture, infrastructure, roles and responsibilities, to implementing the ongoing management and oversight of data collection, verification, storage and usage. Data privacy standards must be respected and adhered to, and trustworthy stewardship must be demonstrated. This level of data governance investment and discipline needs to come from the top down.</p>
<p>Instead of delegating analytics responsibility to lower levels of the organization, it's imperative to reserve a number of executive positions for people with genuine analytics expertise and to propel people with the requisite knowledge and skills into higher ranks. An acid test of capability is this: In a pinch, could the executive run and interpret statistical models on their own? Can they defend their use of data and the conclusions they are drawing? If they can't, they're unlikely to be the person for the executive analytics role.</p>
<h3><strong>2) Develop Routines That Support Analytical Thinking</strong></h3>
<p>This leaves the question of what to do with the non-executive ranks. Non-executives matter because there are many more non-executives than executives, and the best analytics technology falls flat if there isn't a culture of paying attention to data. The trick is realizing that while we're not all going to be elite quantitative analysts, there is nonetheless a level of proficiency that all of us can reach. Fortunately, innate abilities alone do not determine whether we reach this standard. It's also the routines we develop and the activities we engage in when making decisions that determine proficiency. Instead of letting routines and activities develop by chance, dedicate daily time for activities that create analytical habits. Ask yourself, what are the problems you're grappling with that need an analytical answer? What information is needed, where will you get it, and how will it be analyzed to lead to a conclusion? What steps can you take to ensure your conclusions are appropriate? Turning analytical thinking into a dedicated daily activity will encourage the mindset required to thrive. Organizations can further encourage this by rewarding the effort from their employees with project opportunities, positively reinforcing proper analytical techniques even when they result in null findings, and creating learning academies that specify activities workers should complete to increase their analytical capability.</p>
<p>Ensuring proper know-how at the top of the organization and creating an analytical mindset via daily routines throughout will set the stage for success. Organizations that invest in senior-level expertise and reinforce daily analytical activities will be best positioned to take full advantage of the data and technology that abound today and will undoubtedly expand and evolve into the future.</p>
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Executive Summary It's only when deep expertise exists at the top of the org charts that a penchant for evidence-based decision-making will develop pervasively throughout the organization. It's too common to devolve responsibility for analytics modeling to junior positions, which prevents analytical mindsets from really taking hold.

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