Why Automation Is the New Reality in Big Data Initiatives

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Big data is becoming accessible to a much broader range of users thanks to self-service and automation.

Self-service analytics software has been a trend in software development for some time. Conceptually, there is not much novelty about it, though – self-service as a concept has already been applied to fast food joints, financial services and other industries, and the software domain is just customizing it according to its unique needs.

Self-service analytics is specifically aimed at business users who need to easily manipulate data and create analytics without having to depend on technically qualified data personnel such as data scientists. There is a belief that self-service analytics is going to reduce dependence on data scientists. There is also a group of experts that believe that the absolute passing of analytics to the hands of business users can compromise governance and that business users need quality training. Both views have substance. While the forecasts on the self-service analytics market are positive, it is important to train users to use the software properly. There is a lot of scope for business users to learn such software tools. (To learn more on business intelligence and analytics, see Can Big Data Analytics Close the Business Intelligence Gap?)

Self-Service in the Context of Big Data and Business Intelligence (BI)

Think of this use case: In an organization, the customer or market-facing personnel depend hugely on data to make decisions. Now, obtaining customized analytics is not easy because the data volume is huge and comes from multiple sources; it takes specific skills to manipulate data and generate analytics in an understandable format. So, data scientists and other technical people need to be involved. This creates a lot of problems. For example, the bandwidth of the technical personnel and data scientists is divided and too much dependence on technical personnel may delay obtaining analytics, which may hamper decision making.

This problem could be solved by empowering the business users. The business users can be equipped to manipulate data and generate custom reports. Now we are talking about self-service. Self-service in the context of big data and BI is the ability of business users to manipulate and generate analytics as per needs. Business users are independently generating reports just like the self-service concept in a fast food restaurant. Of course, before users can generate reports, the data must be collected, processed and converted into a certain format, which is not the responsibility of the business users.

Self-service has many advantages as well as disadvantages. But a lot of self-service products are now available in the market focused on the business users. These products have certain features in common: intuitive and friendly user interface, customized report generation, and business terminologies. It is assumed that such products have built-in capabilities to accept, mine and process big data without requiring the business user’s participation. So, you can say that self-service software has addressed the use case of empowering business users by reducing (but not eliminating) the dependency on technical personnel. According to Forrester Research, Inc., only 20 percent of the requests to generate reports and queries should be sent to the BI team or the IT department.

Advantages of Self-Service

As might already be obvious, the main advantage of having self-service software is the independence it offers to business users. The users do not need to depend on the BI team or the IT department for running queries or generating reports. This also frees up the technical personnel to focus on other important assignments. Since the business users are able to independently create custom reports and analytics, they are able to find insights and make important decisions more quickly. According to James Foster, general manager of Southeast Asia for Solutions On Demand and high-performance computing at SAS, “As such, it can only be a good thing to have more decision-making capability embedded in the lines of business,” he said. “Plus, the shift to self-service also has a positive effect on IT, freeing them to think more strategically and focus on value-added activities for the company rather than just ‘keeping the lights on.'”


Challenges With Self-Service

The self-service model is based on empowering business users to query and generate analytics while the BI team and the IT department take care of the back-end systems and data integration. However, challenges arise from this model. Technically, it is a complex task to integrate data with the BI systems. BI teams struggle to deliver a single, unified view of the enterprise system. (For more on analytics, see Weighing the Pros and Cons of Real-Time Big Data Analytics.)

The second challenge is about data governance. Giving business users total freedom in using the applications is fraught with risks. For example, it can result in duplicate data and reports, spikes in queries and requests that lead to server breakdown and reports with outdated data or structure. Obviously, there needs to be a balance between data governance policy and user access.

Case Studies

A number of organizations, big and small, have benefited by adopting automation or self-service software. These companies have cut costs, improved productivity and registered higher customer satisfaction. The first case was that of Microsoft call centers. The internal help desk at Microsoft supports more than 105,000 employees, vendors, contractors and clients. It wanted to reduce call volumes, so it deployed several self-service tools, an online support portal, and provided access to knowledge base articles. As a result, Microsoft was able to reduce calls by 15.4 percent at the rate of about $30 per call.

A research conducted by eVergance Partners, LLC, a management consulting company, shows that if a company responds to a customer question online, then the cost is 4 to 40 times lesser than that of having the question answered through a call center.

Getting the Best Out of Self-Service and Automation

First of all, from the perspective of the industry, there is no going back from self-service and automation. But, these opportunities need to be carefully approached. Here are a few tips:

  • Provide a good automation experience to your customers. For example, if your customers use online chatting or website resources instead of a call center, make sure that the process is hassle-free, quick and smooth. If customers have a poor experience, chances are that they may never return.
  • Train the business users to use the applications in compliance with best practices. There should be extensive training given on application handling and there should be clear division of responsibilities between the BI teams and the business users.
  • Build the automation tools incrementally and use your experience in improving them. According to Allen Bonde, senior vice president of strategy and marketing at eVergance, “Take advantage of the plumbing that you have built up over the last decade.” There are a lot of things that you can do such as payroll business processes, automated interfaces for human resources, and call dispatch requests for mobile field service teams. That would not guarantee customer acquisition or retention, though. Bonde adds, “Don’t assume that just because ‘you build it, they will come.'”


Self-service and automation in industries that deal with big data are considered huge opportunities. However, companies need to be careful while using these chances because careless execution may result in loss of reputation and customers. Proper training and intelligent policies are the way to move ahead.


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…