Produced at an astounding rate, data is everywhere. From purchase transactions, internet search records, social media posts, and form entries to GPS cell phone tracking, climate sensors, digital pictures, and internal company information, we create 2.5 quintillion bytes of data every day. Among that data are consumer transactions and purchasing behavior. Loyalty program members surpass 2.65 billion in the US alone and the significance of the data collected is staggering. Marketlinereports, “Big Data analytics has become an absolutely essential part of loyalty programs, allowing retailers to spot patterns in people's buying habits that would be otherwise hidden.”
In business, data is vital to decision-making. A recent Economist Intelligence Unit reportsurveyed 476 senior executives worldwide and found Big Data essential to growing their operations. Of the executives surveyed, 83 percent reported their firms have used data to make existing products or services more profitable. Data supports organizational efforts and informs the ability to meet key performance indicators (KPIs).
Most businesses have realized the importance of collecting copious amounts of data. The use of this data, explained in a report from the McKinsey Global Institute, “will become a key basis of competition and growth for individual firms.” Big Data is what allows businesses to get smarter and more predictive about each and every customer. Big Data allows predictive text searches on Google, product recommendations on websites like Amazon, and emails personalized to individual customers. By leveraging data-driven strategies, businesses can create 1:1 customer journeys, providing the right information and the right offers at the right time.
Big Data and analytics not only help companies increase their bottom line by providing personalized and predictive customer information, in many cases, Business Intelligence and analytics have lead to the creation of new products and services. A surveyfrom The Economist showed that companies with Big Data practices allowed them to use their data to provide a new product or service. Of the companies surveyed, 60 percent are already generating income from the data they gather and analyze and 69 percent of the executives surveyed felt that there is a case for starting a new business unit dedicated to developing data-related products or services.
Data scientists are changing industries. Data scientists use data analytic models to predict earthquakes and stop crimes. Unstructured medical data can be analyzed with digital patient records to improve healthcare. Analyzing weather patternsmeans more efficient airline scheduling, improved verification of insurance claims, and life-saving severe storm warnings. Likewise, data scientists are making businesses more profitable.
While data is the essential building block of a Business Intelligence strategy, Big Data is only useful to a company when it can make sense of the data and then act on it. That’s where the role of a data scientist, data analyst, or Business Intelligence developers comes in. If your company doesn’t have someone with Business Intelligence training or data analytics training--the skills to analyze data--it’s probably time to consider it.
General titles of data analytics and Business Intelligence professionals vary, but the most common data analysis job titles are Business Analyst, Business Systems Analyst, and Data Scientist.
Business Analysts will likely focus on establishing objectives and the scope of business and IT systems, reports Mastersindatascience.org. Business Analysts tackle each job by employing their know-how in business administration, finance or management. The benefit of this approach is that they can see the “big picture” – the scope of the challenge within the industry.
Business Systems Analysts come at the problem with extensive knowledge of IT processes. And, although the roles are often merged in small companies, the job of a Business Systems Analyst may be much more technical in nature than a conventional analyst. Often, a Business Systems Analyst can also design programs and write code.
Data Scientists are highly trained in mathematics and computer science. But because Big Data analytics is a relatively new field, there are few formal training programs devoted entirely to data science, most of which are master’s level programs. Some data scientists are trained business analysts while others have strong systems backgrounds.
Whether your company needs a data analysis professional with a strong business background or someone with Business Intelligence skills steeped in IT systems, coding, and design, understand there is an overall shortage of people with business analytics training.
According to the McKinsey Report, “There will be a shortage of talent necessary for organizations to take advantage of Big Data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of Big Data to make effective decisions.” What this means to businesses is that, while the need for Business Intelligence skills is undisputed, the ability to hire people with the needed analytic skillset is limited.
And, even those trained in data science may not be absolutely perfect for your company. Amy Gershkoff, the chief data officer at Zynga and adjunct professor at the University of California at Berkeley explains, “Without training in...other areas, data scientists may be capable of designing an algorithm that is mathematically elegant, but doesn’t make strategic sense for the business.” Computer science skills are essential, but a working knowledge of your company’s processes and needs is also paramount to implementing successful business intelligence strategies.
To maximize the advantages of Business Intelligence and Big Data to your company, it is essential to hire the right data analysis skill set. Consider the following strategies as you find someone to help make sense of your data:
Use connections. Hiring based on connections and networking usually produces better results. Employees who join a company based on the recommendations of their friends usually stay longer, decreasing turnover issues; 43 percent of hires through referrals & company career pages stay with the company over 3 years. When hiring, you want to get to know your best employees’ friends and contacts. After all, most “A Players” hang out with other “A Players.”
Another recruiting method is social media. Of employers who used this platform to recruit, 49 percent reported an increase in the overall quality of candidates. Organic hiring produces better results. Why? Again, because people with top skills attract other people with top skills.
However, keep in mind there is a difference between asking your employees to post a job on their social media and asking a recruiter to do the same. Most recruiters don’t have access to the people you want to hire. Of recruiters, 94 percent are on LinkedIn, but only 36% of candidates are also on the site.
Interview with purpose. Once you’ve found some viable candidates with the right skillset, it’s time to interview them. Geoff Smart and Randy Street spent over 1300 hours interviewing CEOs and managers in preparation for their book, Who: The A Method for Hiring. The book is a report on their findings of how to improve the interview process in order to find the best talent for the job.
Hiring the wrong person can be costly. Smart and Street explain, “According to studies we’ve done with our clients, the average hiring mistake costs fifteen times an employee’s base salary in hard costs and productivity loss.” They recommend a series of four interviews: a screening, a TopGrading, a focused, and a reference interview.
Train and hire from within. According to a survey in CMSWire, almost 70 percent of respondents—more than 500 senior executives in organizations around the world—said recruiting and retaining people with a data analysis skillset is “somewhat” to “very” difficult. But what do you do if there are just no viable data science candidates?
The answer is train the people you already have. One in three respondents in the CMSWire survey said they value having programs in place to train employees. Gershkoff agrees that launching programs to train analysts as data scientists is a very practical solution--one that has seen success at her company. “Many organizations are flush with analysts who typically have some data science skills in key areas such as statistics,” explains Gershkoff. Her company recently initiated a Data Science Transition Program, which apprentices data analysts to a data scientist for twelve months. The candidates learn on the job and take online data science courses. At the end of the program, says Gershkoff, the participant becomes an Associate Data Scientist at the company.
Big Data means everything to companies because it allows them to create personalized and predictive experiences for their customers. However, hiring and retaining people with data analytics and Business Intelligence skills is a challenge that will only increase in the coming years. Having the right business intelligence software is a first step in solving the data analysis puzzle.
Your company is probably sitting on a fortune’s worth of data that can give you deeper customer insights and pinpoint actions to streamline processes. You need someone who can mine that data. Finding people with advanced data analytics skills allows you to make Big Data work for you. Using customizable reports and real-time statistics make it possible to leverage data to make informed marketing, customer service, and sales decisions.
Today’s business intelligence is technology-driven with Big Data in the driver’s seat. The biggest challenge for most companies in this Big Data frontier is finding the right people with the required skills and data analytics training who can help your company leverage Business Intelligence and analytics to grow sales and streamline processes.
Given the shortage of data scientists, the answer many companies are finding suitable is to train from within. Training a current employee in Big Data analytics is often a superior solution because your employee already understands your operations and can enable your company to use Big Data to reach your goals.