Have you ever wondered how Google knows exactly what you’re looking for, why Netflix’s recommendations are spot-on, and how Pandora is finding just the right tune? That’s the brilliance of machine learning technology at work.
According to Stanford University, machine learning is “the science of getting computers to act without being explicitly programmed to do so.” Machine learning works in tandem with data science to provide the ability for machines to learn, develop, and make decisions based on user behavior. So, what does that mean to individuals and executives in the business world?
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Because companies across all industries are realizing the need for data in evidence-based decision making over gut instinct, they’re increasingly relying on more minds, teamwork, and tighter collaboration for confidence and trust in analytical outcomes. Today, machine learning is improving the process by being a new “mind” to supplement and enhance our human thought.
Machine learning is now being leveraged to prep data faster, comb through datasets more quickly, and enhance collaboration among departments and team members. The end result is increased productivity, faster time to insight, and improved data quality. Here are three ways machine learning is achieving the goal of creating better business operations and outcomes.
1-Machine Learning Enables More Efficient Corporate ‘Data Marketplaces
Spreadsheets are—and will likely remain—the most common analytics tool. However, they are flawed. Spreadsheets are riddled with accessibility and governance issues, lack collaboration and socialization features, and require extensive training to leverage them effectively.
A study by TDWI, titled “Reducing Inefficiency and Increasing the Value of Analytics and Business Intelligence,” found that companies are increasingly moving to a data catalog or are creating a corporate data marketplace. In fact, 44% of companies have plans to use a central data catalog or marketplace within the next 3 years, the study said.
How are these platforms different from the data warehouses of old? They offer a centralized, easily accessible resource that indexes usable reports, analytics (including models and formulas), visualizations, and other quality data artifacts. The emphasis is on sharing and collaboration—allowing the system to be democratically maintained and continually improved upon by the business, while maintaining governance and control IT demands.
Machine learning is incredibly powerful when applied to these data marketplaces. Similar to the way people shop for a new book on Amazon, analysts can leverage catalogs to find, preview, and learn more about a dataset’s origin, usage, and data lineage (which is defined as how the data has been altered and by whom). They can also access datasets with ease, explore related data collections, and share datasets among co-workers. Machine learning technology assists by suggesting relevant datasets based on the ones previously accessed by other colleagues or recommending a certain data model or report to pursue. It can also make recommendations to users on how they can get more from their data. If this sounds similar to the technology Google, Netflix, and Pandora are using, that’s because it is.
2-Machine Learning Improves Productivity
It’s no secret that most users and analysts spend more time manually combing, cleansing, and transforming data than actually extracting value through visualization, reporting, and analysis. In fact, nearly half of the TDWI survey respondents said they spend at least 61% of their time finding and prepping data. However, the latest trend in data analytics is changing all that: Machine learning is significantly increasing the time users can devote to value-added activities by standardizing and automating processes for data preparation and analytics.
In a centralized data marketplace, machine learning can be leveraged to provide recommendations based on what other users have done to the data in the past. This means there’s less time spent evaluating the changes that need to be made and more time actually making the changes. This not only increases productivity and streamlines processes for all data users, it means that analysts are more likely to be organizing and formatting data consistently across the organization. The result is increased trust in data outcomes and less time debating the validity of datasets so that outcomes can more confidently be used to inform bold and decisive actions.
3-Machine Learning Drives Data Democratization
Due to the widespread adoption of business intelligence (BI) and self-service analytics, analytical output of non-technical business users is expected to surpass that of professional, trained data scientists very soon, according to a recent study published in PC Magazine.
Though these self-service tools promote agility, put more data into the hands of more users, and allow for streamlined insight derivation, as a result, it will also become increasingly necessary for organizations to ensure analyses aren’t produced in silos. Fortunately for executives, machine learning will shorten the path to data democratization, break down silos, and improve collaboration.
Machine learning digitally fingerprints users and analyzes content and behavior to generate personalized rankings of specific assets in both trustworthiness and relevance to each user. Ultimately, this ensures users benefit from earlier work produced by more experienced analysts with data literacy who tackled similar problems.
In the midst of a competitive labor market, machine learning can ensure quality analysis makes its way into the hands of every user (especially junior ones) who will benefit. Thanks to machine learning, analytics will be in the hands of every analyst in a way that’s trustworthy and reliable to the company as well.
What’s Ahead
As the need for analytical insight grows, companies across all industries will be in search of processes that increase collaboration and productivity, reduce data errors, and instill trust in data-driven decisions.
Studies indicate that data marketplaces, data preparation, and data democratization will continue to increase in popularity with time. Data marketplaces will allow users to access, collaborate, and discover new datasets more efficiently. Data preparation will help cut down on errors and time spent finding, cleansing, and transforming data; data democratization will help put analytics in the hands of all users within the enterprise.
But behind major analytics trends, machine learning will be there leading the charge. By analyzing user behavior, providing smart recommendations, and instilling confidence behind every data-based decision, the future will be data-driven, but powered by machine learning. Machine learning is not only enhancing the way we surf the web and consume media, it’s simplifying the way we work too.
As chief product officer, Jon Pilkington brings more than 2 decades of business analytics experience to Datawatch, including 18 years in the business intelligence market. To learn more about Pilkington’s views on data preparation and business intelligence technologies, read his posts on LinkedIn and the Datawatch Blog, or follow him on Twitter @Jon_Pilkington.