Advances in modern computing have made it possible to analyze more data than ever before. Data analysis automation has the potential to accelerate digital transformations for companies across a wide range of industries. Before you take the first steps toward that future, though, it’s wise to understand what uses cases data analysis automation is best suited for and which ones may present challenges. It’s also a good idea to understand the digital transformation process. Let’s take a look at how your company can benefit from both.
Where Data Analysis Automation Excels
Automating the analysis of data does not provide uniform results. Automation generally works best in situations where data lends itself to many iterations of analysis. This is one of the main reasons that preparing data is a critical part of the process.
If the information going into the system presents problems, the odds of getting useful analysis drop significantly. While many new approaches, such as the use of neural networks to conduct unsupervised machine learning, can iron out some of the uglier bits, providing clean inputs remains the easiest way to not have trouble. This means ensuring that data fields are prepped to be uniform, scrubbing data for things like unreadable characters and performing initial analysis to remove anomalies and outliers.
Highly prepared data is the best sort to use in an automated process. For example, consistently formatted datasets from a trusted government source, such as weather data, tend to require less preprocessing.
The amount of available data also matters. If you’re trying to glean information from a couple of thousand data points, the data set may be too paltry for use. That’s especially the case once data points have been scrubbed from the set. More robust data sets, such as Amazon’s studies of billions of searches for products, also tend to lend themselves better to data analysis automation.
Developing a Culture That Values Data
Digital transformation calls for more than just hiring a few programmers, engineers and data scientists. At its core, the transformation of a company calls for a shift in its culture. This process does not occur overnight, and it requires on-boarding many employees and decision-makers. Hiring practices have to incorporate a data-centric worldview as being just as important as hiring “self-starters” and “team players.”
As painful as it may be to do so, companies also have to confront off-boarding folks who refuse to come along with the transformation. While an honest effort should be made to onboard all personnel, there will come a point where providing severance packages to those who struggle with the change will be necessary.
Choosing a Direction
A company must make an overt effort to pick a direction when it adopts data analysis automation. While it might seem easier to leave this up to the data people, that risks creating a hodgepodge of semi-compatible systems and cultures within your organization. Planning needs to be done to produce documents that outline what the organization’s philosophies are and the kinds of hardware and software it’ll use to accomplish its goals.
There are many major frameworks on the market today. Plenty of them are free and open-source, and those frameworks are often robust enough to do the heavy lifting a company requires. For example, the combination of Tensorflow and Python is wildly popular. These two are often used in conjunction with nVidia’s CUDA Toolkit for GPU acceleration.
Each choice will have its pros and cons. A software stack built on Linux, Python, Tensorflow and CUDA, for example, will call for engineers with specific job skills. Likewise, maintaining and updating software and hardware will require close attention to the requirements of the environment. A new version of CUDA, for example, might open up opportunities for machine vision analysis to be exploited, but it may also call for recompiling existing software and code to operate properly. Diving into such changes willy-nilly can cause the entire software stack to collapse.
Good planning documents should provide fair consideration to the available options. A reasonable comparison might be made between the virtues of OpenCL versus CUDA. There should be a good reason for the final choice you arrive at, such as the relative costs of doing GPU acceleration on nVidida versus AMD hardware.
Compliance
A disconnect between data analysis automation and the real-world things data points represent is a major risk. In an increasingly strict regulatory environment, failures of compliance come with major costs. It’s prudent for an organization to not only think about how it will acquire and process data, but how it will protect the privacy and well-being of people who may be represented by data points. Each company should also consider its own values during the digital transformation process.
Conclusion
The hardest part of automating analysis is ramping up capabilities. Your company will have to plan for a variety of challenges, such as how it will store and archive the data, what will be done with analysis and how it will report its work.
Analysis, though, lends itself to many business cases. Once your company is generating usable work products, you can also begin to develop new business models. For some companies, the focus will be on improving efficiency and processes. Others will discover that selling analysis to other parties is a viable business model in its own right. In time, you will find the returns from building out automated analytics capabilities tend to compound very quickly once you get rolling.
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