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How Big Data Can Help in Self-Service Analytics

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With the help of self-service analytics, even people who don't specialize in data science can interpret data.

Self-service is a part of our daily lives. People are empowered to do their tasks themselves, like monetary transactions at an ATM, pumping gas at gas stations, check-in at airports and many other similar activities. So, on one side it reduces the operational costs of an organization, and on the other side, it generates a huge volume of data (typically big data). This data has a lot of potential in the world of analytics. Organizations are extracting meaningful insights from such self-service data and generating more business opportunities out of it.

What Is Self-Service Data?

Self-service data analytics is actually a type of advanced analytics that can enable businesses to use the large amount of data/cloud data for finding the best business prospects and choices. This is also easy enough to be used by those without a very clear statistical or technological background.

Self-service analytics allows the user to scan large data dumps, visualize the data and use it to get useful insights for their business. This also allows businesses to ensure that their daily requirements are being fulfilled, and to know about other requirements that may arise. The insights come from large business-owned data reserves, which in turn comes from various transactional data, web logs, sensor data and social media data. Self-service business intelligence is a subset of self-service data, which helps a business to make important decisions based on the data.

How Self-Service Data Is Helping Analytics

Nowadays, many companies are making software which allows business users to collect information from a variety of sources. Such software can be difficult to use. It has dashboards, which allows the analyst to query data and analyze it. Such software, due to its complexity and steep learning curve, can be used only by highly trained data analysts, also called data scientists. (To learn more about data scientists, see Data Scientists: The New Rock Stars of the Tech World.)

On the contrary, self-service analytics has been introduced in order to help businesses continue the effective analysis of data, without the need for any trained professionals, as data scientists are becoming very hard to find nowadays. This will also allow business users to directly handle the data, which they can easily manipulate according to their needs and preferences. So, self-service data is allowing business users to make good decisions based on powerful, but easy-to-do analytics.

How BI Is Impacted by Self-Service Data

The needs of businesses always remains the same, though the technology required to achieve those goals changes with time and currently available technologies. Nowadays, the amount of data has also increased manyfold. Such data is very complex too, as it comes from many different sources.


However, with the advent of self-service data analytics, large amounts of data can be easily analyzed. Also, a special “semantic layer” allows even normal business users to easily access the data and use it, as it resolves the complexity of the data. This has resulted in easier business decisions, which are based on accurate data analysis and is giving a new name to business intelligence. (To learn the basics of BI, read An Introduction to Business Intelligence.)

What Are the Challenges?

Integrating self-service business intelligence tools must be very delicately done, because while it can allow business users to easily carry out business-intelligence-related tasks, it requires IT professionals to manage their data. However, integrating the data can be very difficult, as it is with any BI solution.

Another major challenge is that of governance of data. Proper security measures and data governance are essential for self-service business intelligence. Thus, businesses using self-service business intelligence should be able to guard data against extreme freedom of the users of that business. Extreme freedom could mean that the users hit many queries at once, which is enough to cripple any server. They could also put the security of the data at risk, mostly by third-party access.

How Business Is Gaining From Self-Service BI

Self-service data is proving to be very useful for businesses. They can use self-service analytics for quick and complete analysis of large amounts of cloud data available in stores. This can help a business by improving the reporting of the business.

It can also help in decreasing data overloads. The analyzed data can be used later for creating charts and marketing dashboards to improve business intelligence. This also allows the business to make complex decisions easily and carry out day-to-day tasks quickly. If you integrate it with a rules engine, then it can become a really powerful business tool which can further help the business intelligence of that organization understand their current goals. So, business is gaining quite a lot from self-service BI.

Use Cases

Everyone knows that the existing BI tools aren’t enough for meeting all the business requirements. However, with the introduction of self-service business intelligence, everything has seemed to change. Earlier, businesses had to consult with a data scientist repeatedly for any query or for asking questions. But now, this situation has reversed. Business users can now easily study the business insights and operate tools without the help of a data scientist. So, obviously many businesses have begun using self-service business intelligence for easier insights and faster work. Some of the use cases of self-service business intelligence are given below.

Boston College Libraries

The Boston College University Libraries are educational resource centers, which consist of three libraries, with more than 2.5 million books. However, the system needed self-service reporting for properly allocating its budget and ensuring mobile access.

After implementing the self-service solution, about 14,000 more students were added to its student base. They could access its vast resources from anywhere, and at anytime.


Motionsoft is a financial solutions provider for businesses in the health and wellness sector. Its old Crystal reporting system wasn’t powerful enough for interactive dashboards and web-based reporting, so it chose self-servicing solutions like Logi Ad Hoc and Logi Info. The solutions were very powerful and allowed many self-servicing capabilities.


Hylant is a provider of insurance brokerages which are extremely cost-effective. They also provide risk management solutions for a variety of businesses. They needed to eliminate any ad hoc changes by enhancing the report request process. They also needed to help the users create their own reports.

So, they used Logi’s self-service module, which allowed their clients to query and manage their own reports very easily, helping in better decision making.


Self-service is really a turning point in the field of business analytics. Self-help is the best help, which we all know, and with the help of self-service business analytics, we can realize this. Gone are the days when business users had to consult data scientists for any question or for any task. Now, users can easily carry out their own analysis accurately, which increases the speed of the business too. Also, as experienced data scientists are becoming harder to find, there is a need for easier operations which can be done by even inexperienced users through proper training. Though there are certain problems, like security problems, data integrity issues, etc., this self-service solution will evolve and hopefully eliminate them automatically. So, it is safe to conclude that self-service business intelligence will be the business intelligence of the future.


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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…