Data is now at the heart of the digital transformations occurring across the economy and building an advanced data analytics team is one of the keys to competitive advantage. Whether you plan to simply disrupt your own industry or expand into an adjacent segment, data is the key to unlocking opportunity. Data has become a critical corporate asset, and business leaders want to capitalize on the information they hold. But its value is tied to how it’s analysed and by whom. One dataset may be of little value, while another may contain the key to launching a new product line or cracking a challenging marketing question. One might affect only a small percentage of a company’s revenue, while another could reveal an opportunity for significant future growth. How do firms find out? Analytics.
Yet across the board, companies report that finding the right talent is the biggest hurdle they face in trying to integrate data and analytics into their existing operations. Data scientists, in particular, are in high demand. This is a challenge that is likely to continue in the near term. The forecast from thought leaders like McKinsey and IBM is that the scarcity of data analytics skills will get far worse before it starts to get better.
One countervailing force that can ease this imbalance is the automation of data preparation. According to a recent survey on data science and machine learning by Kaggle, the number one challenge that business analysts and data scientists report is poor quality data. Their goal is to leverage data into actionable insights, yet they are hampered when the quality of the data they are working with impacts the results. And the more they turn to analytical applications, predictive analytics, and artificial intelligence, the worse the problem gets. Building and testing algorithms requires good quality data so that the quality and accuracy of the outcomes can be trusted. In addition, the increasing volume of available data, while promising greater insight and model accuracy, also increases the amount of time required for data preparation.
Reportedly, data preparation by business and data analysts represents between 50% and 80% of analytic project schedules, depending on which market research firm you consult. Either way that’s a lot of time for relatively high-paid employees to be spending on fixing data quality issues.
In addition, it’s not what they were hired to do! A quick scan of resent analyst job descriptions reveals a host of interesting and business valuable responsibilities where their efforts and results are required to inform strategic decision making. Responsibilities such as:
• Identifying, compiling, analyzing and documenting requirements and critical success factors
• Leading discovery, resolving difficult challenges, and using advanced techniques and technology
• Developing business cases and analyzing current states in terms of people, process and product.
• Directing cross-functional efforts, collaborating, and supporting projects through their life cycle, with emphasis on the analysis stage
• Serving as a subject matter expert, contributing to the creation and completion of results, documents, and presentations for senior management.
Nowhere will you find “spending three quarters of your time doing data preparation” in an analyst job spec!
For anyone who is asked to undertake a data analytics project the first questions are often: “What’s the quality of the data we are using? If I analyze it, can I trust the results I get or will I need to spend days prepping the data?” Data and business analysts like participating in strategic projects, like getting the answers to tough questions, and like to feel they are valued. When they must spend a lot of their time cleaning and standardizing data so that their analytic tools and apps will work, they feel it’s a waste of their time and become frustrated.
In comes Experian Aperture Data Studio, Experian’s latest data quality management solution. Aperture Data Studio automates data prep work, leveraging an easy-to-use, drag-and-drop user interface that requires no programming knowledge. With automation, analysts still need to be involved with data prep but more so as a subject matter expert to verify rather than do all the work. Aperture Data Studio rapidly profiles data and finds where the problems are. Then its intuitive interface allows workflows to be created quickly and problems to be speedily resolved. The next time users need to pull the same data, or when new data is added, they can simply rerun the workflow to get updated results. Experian Aperture Data Studio doesn’t do the analysis but it does gather and prepare the data so analysts can do their work and get their answers quickly.
In fact, Aperture Data Studio is the ideal solution for consolidating and creating a trusted source of data. When you need to gain deep customer insight from your data or achieve a single customer view, Aperture Data Studio can help. When you need to prep for a data migration, Aperture Data Studio can help. And when you need to ensure data quality for governance programs and to comply with policies, rules, and regulations, Aperture Data Studio can help.
Are you interested in automating your data prep? Check out how Aperture Data Studio works.