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Four Ways to Make a Data Scientist a True Business Asset

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AnonymousBy Charlotte Blacklock 3 years ago
Home  /  Data  /  Four Ways to Make a Data Scientist a True Business Asset
Data Scientists Business Asset

Within the past couple of decades, businesses have become highly cognizant of the value that big data can bring. More recently, they have realized that access to big data in and of itself is insufficient – volumes of data by themselves do not suggest how to improve a business, and thus it is also necessary for businesses to acquire someone who can interpret the data. Enter the data scientist, now known as one of the most desirable positions of the twenty-first century. Even more recently, however, businesses have come to realize that simply hiring data scientists to interpret data is not necessarily sufficient to drive business outcomes, either. Interpretation of data without direction or context leaves business leaders with little more idea of how to use that data than they had before.

The ultimate goal of the data scientist, therefore, cannot simply be to interpret data and produce insight; it must be to provide the exact information needed to improve the business. Thus data scientists who are truly business assets require a unique set of skills. Traditionally, data scientists are known as the people who crunch the numbers, drawing conclusions from vast amounts of quantitative data. In reality, however, data scientists are much more – with the information they gather, they have the power to equip business leaders to make informed decisions that can affect the entire future of the business. Businesses leaders who want to maximize the value they glean from data scientists, therefore, should take steps to ensure that their data scientists are oriented around business outcomes. There are a number of things that data scientists can do to become business-outcome-oriented.

Tell Compelling Stories

First, data scientists need to learn to tell stories with the data instead of simply drawing conclusions that are devoid of context. Without context, data scientists can never truly help business leaders understand what the data is saying and what to do with it. Similarly, data scientists need to present their conclusions in such a way that leaders who may not be fluent in data scientist speak can still understand exactly what is going on. According to an article in the Harvard Business Review, “Without a human frame, like photos or words that make emotion salient, data will only confuse, and certainly won’t lead to smart organizational behavior.” Big data needs to be humanized before it can truly be understandable, so the successful data scientist will be one who understands how to present the data in such a human frame.

At the same time, the stories the data scientist tells need to be not only understandable but also compelling. In order to be compelling, the stories need to have plenty of technical detail to back their claims. The key for the data scientist is to find the balance between providing the necessary technical detail and still presenting that detail in an easily understandable way. Thus data scientists need a specific skill set that allows them to communicate effectively, read data effectively, and translate that data into a compelling story.

Transform Data into Action Steps

In addition to telling stories with data, data scientists need to know how to transform data into clear action steps. In order to do so, they need to have a thorough understanding of the organization and what it has the capacity to accomplish. Unclear objectives or objectives that the organization cannot accomplish are equally as useless as no objectives at all.

Moreover, the data scientist’s findings should prove the value they bring to the organization. Another article in the Harvard Business Review outlines how benchmarking with metrics is necessary to this process. “The best data scientists immediately speak in terms of business metrics because they understand that their work has to have value for the organization, not just be interesting to data scientists.” Making sure to relate findings to benchmarking and metrics demonstrates the value of the work and helps the business attain its strategic goals.

Use Tools to Scale

Although the business world widely recognizes the value that data scientists can bring to an organization, some are beginning to doubt the capacity of data scientists to scale their work without assistance from automation and software tools. Because the approach to interpreting big data is typically reliant almost entirely on human skill, the process must move at the speed of the data scientist. Moreover, as an article titled “You Need an Algorithm, Not a Data Scientist” in the Harvard Business Review points out, businesses frequently change too quickly and too imperceptibly for the data scientist to capture every last detail. And data scientists are expensive, making an average starting salary of around $118,000, according to Glassdoor. Certainly it is broadly understood that data scientists spend a good deal of their valuable time cleansing, sorting, and preparing data for analysis.

In order to fully benefit from the expertise of data scientists, therefore, businesses would do well to adopt tools that augment the skill of the data scientists and automate data cleansing and analysis, allowing the data scientists to do more of the work of ensuring that analysis is translated into clear and achievable business objectives.

Involve the Organization

Finally, in order to ensure that their work brings lasting value to the business, data scientists should work with the business as a whole to ensure that the entire organization is thinking and acting in a data-oriented way. With the entire organization on board, understanding the value of big data and trained on what they can do in their day to day operations to support the use of it for informing business strategy, businesses will be able to maximize the value they get from data scientists. Another Harvard Business Review article recounts how involving the organization translates into success. “The smartest thing I’ve seen organizations start doing is seed-fund and empower small cross-functional data-oriented teams explicitly charged with delivering tangible and measurable data-driven benefits in relatively short periods of time. . . . The goal is to make all of the organization — not just the geeks and quants — more conversant in how to align probability, statistics, technology and business value creation.” Data science needs to become a part of the organization’s DNA rather than the responsibility of just a few people.

Data scientists are valuable and highly sought after, but their value is not exclusive to their skill set. Businesses who want their data scientists to become true assets to the business, adding perceptible value and helping achieve strategic business outcomes, will ensure that the data scientists they hire tell stories, transform data into directives, use automation tools, and help the entire organization to become more data-oriented.

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