It is a truth universally acknowledged that the modern business is awash with data. This data could be the most valuable asset a company has- but only if it’s used right. Last year, McKinsey estimated big data initiatives in the US healthcare system “could account for $300 billion to $450 billion in reduced health-care spending, or 12 to 17 percent of the $2.6 trillion baseline in US health-care costs”. However, bad data is estimated to be costing the US roughly $3.1 trillion a year.
What’s become increasingly clear is that the proper processing and analysis of data is where the value lies- and this is where the data scientist enters the picture. Most executives have heard that data science is sexy, that data scientists are the new superheroes- and yet, many are unclear about how a data science specialist could add value to their particular enterprise. Here are five common ways in which data scientists can add value to businesses:
1. Decision Making With Quantifiable, Data-Driven Evidence
For years, the making and breaking of executives has been sheer gut instinct. It was not uncommon for million- and -billion- dollar deals to be accepted or rejected based on the intuition & instinct of a few powerful figures- to varying degrees of success. The gathering and analysis of data from multiple different channels is ruling out the need for such high-stakes risk taking.
One company who were following the legacy approach to high-powered decision making were Ford, back in 2006. They closed the year with a $12.6 billion loss, the largest in the company’s history. It was clear that something had to change- and something did.
In the words of Ford’s Chief Data Scientist Mike Cavaretta, 2006-07 “was around the time Alan Mulally was brought on. He brought with him this idea that important decisions within the company had to be based on data. He forged that from the very beginning, and from the top down. It really didn’t take a long time for people to realize that if the new CEO is asking, “Hey where is the data you are basing your decision on?”, you’d better go out and find the data, and have a good reason why that data matters to this particular decision.”
Within three years of implementing a top-down data-driven approach, Ford was turning a profit again. In 2009, they sold 2.3 million cars, the only company to exceed the 2 million mark since 2007. The kind of impartial, data-driven counsel a data science team can provide can lead to informed and timely decision-making in any enterprise.
2. Testing Those Decisions
Making decisions and implementing change is only half of the battle; it’s vital to know how those changes affected the company. Having someone who can measure key metrics related to important changes and quantify their success (or lack thereof) is an immensely valuable tool for any organisation.
Take startup Carelogger, for instance. They made three experimental changes to the layout of their website, and tracked the success via A/B testing. They found that the optimum combination of these three variables increased their conversion rates by a staggering 72%. Making changes is vital in every business; but so is assigning someone to figure out if those changes added real value to your enterprise.
3. Identifying and Refining Target Audiences
Google Analytics. Social media. Customer surveys. Loyalty programmes. Many businesses will have at least one of these treasure troves of customer data- but if you’re not using it to identify your key demographics, this data is going to waste.
Gone are the days where advertising and sales were the sole remit of creative-thinking Mad Men types, who wildly conjectured who a product’s target audience might be, and constructed campaigns which may appeal to them. Everything from your social media profiles to your website visitor reports contains data which can help you pinpoint your target audience- and therefore target them more effectively.
Even if you have gone as far as roughly identifying your demographics, a data scientist can identify your key groups with laser precision through careful analysis of disparate data sources. This in-depth knowledge can help you tailor products and services to your customer groups- and help your profit margins to flourish.
4.Translating Your Data into Actionable Insights
In truly successful data-driven organisations, the data isn’t merely in the hands of data scientists, passing down decisions and stats like some sort of data oracle. Data has to be at the fingertips of every decision-maker, and ideally, every employee. The data scientist plays a vital role in the process of translating the raw data into insights, which can be transformed into actionable applications. Forrester highlighted that 38% of businesses struggle to communicate and interpret the results of analytics; this is a key pain point that a data scientist can help to address.
5. Recruiting the Right Talent for Your Organisation
Combing through CVs was once considered a staple of the recruiter’s day to day tasks, but data science is changing this. With a wealth of information on talent available to businesses today- through social media, corporate databases and job search websites- a data science specialist can wrangle this data to hunt out the candidates who fit best with your company’s needs. Recruitment need no longer be a time-consuming and exhaustive process of human review, with little guarantee that an applicant will be right for the job until they’re sitting in the office chair. Through data mining the vast amount of data talent already available, in-house processing of CVs and applications, and even sophisticated data-driven aptitude tests and games, data science can help your recruitment team make speedier and more accurate selections.
Ultimately, data science can add value to your business by adding insights and statistics across your workflow- from hiring new candidates to helping the most senior staff make better-informed decisions. Data science can add value across all industries; it’s about finding your pain points, and identifying exactly how you can put a data scientist to work for you.
Eileen has five years’ experience in journalism and editing for a range of online publications. She has a degree in English Literature from the University of Exeter, and is particularly interested in big data’s application in humanities. She is a native of Shropshire, United Kingdom.
(Image credit: epsos.de)