As data discovery has been a big emerging trend in the ICT domain, there are growing opportunities in the business analytics sector and innovative thinking can deliver great results for partners active in this segment.
Emergence Of Data Discovery
Most traditional business intelligence platforms only help people answer predetermined questions. They are report-centric and focus on enterprise IT requirements. This mindset is changing, and data discovery is gaining strong traction in the market – especially in the solutions space.
Data discovery involves exploring data freely, discovering insights from a set of data and analyzing and presenting those insights in an interactive and visual format.
Data discovery makes it easy for enterprise users without any technical skills to run queries and analysis. The users can learn at each step of the process and can come up with the next steps on their own. Solution providers working in the field of data discovery hold great chance of increasing their customer engagement with the help of positioning right solutions.
According to Markets and Markets, the n-memory technology market will increase from $2.2 billion in 2013 to $13.2 billion by 2018 due to decline in the cost of semiconductor technologies and rising need for real-time data analysis and managing voluminous databases.
Enterprise-Level Business Analytics
The greater emphasis on the ease of use and visualization when it comes to business intelligence software has fueled growth for data driven analytics today. According to IDC, the worldwide business analytics market generated $37.7 billion in 2013 and is expected to grow to around $59.2 billion in 2018. This translates to a 9.4% compounded annual growth rate for the forecast period.
Gartner introduced the concept of governed data discovery and it refers to platforms that can address enterprise-level IT requirements as well as assist business users (non-IT) in data discovery. No vendor has addressed both these aspects so far but there is an indication that this convergence will happen sooner than later and solution providers can play a key role here.
Moving forward, data discovery strives to find the right balance between allowing creativity so that end users can experiment with the data and maintaining just enough centralized control so that the enterprise tasks can run smoothly.
Building Analytics Solutions – Visualization – to Discovery
It is increasingly getting about daty, data,data and data but – what do I do with it. How do I make sense of it that is useful to the business? More importantly, how do I tell the story now that the solution is built? Say help to VISUALIZATION. Virtually any software package today has some kind of visualization, even rudimentary tools such as Excel.
Too often the general “business” public is bombarded with promotions and ads that visualization tools are the panacea for “telling the analytics story”. Visualization is often presented as a means of overcoming the insurmountable communication barriers that exist between the data scientist and the business person. And hence, visualization delivers the right path towards data discovery in the enterprise space.
These typically common prejudices exist with business people belaboring the fact that the data scientist does not understand the business problem while data scientists clamor about the lack of attention to “math” and “data” being an inhibiting factor in the creation of effective solutions.
With visualization, software vendors present their products as a means of “putting a picture” on the data science solutions. But this is easier said than done. The real work is the data lying beneath these visualization tools.
The data must still be “worked” in order to provide that visualization capability. In order to obtain a better appreciation of this, let’s look at the construction industry where over 90% of the house’s real value arises from the right engineering and architectural processes.
Yet, it is the 10% or less which represent the “finish” of the house or how the house visually appears. In a way, the so-called “finish” of the house is akin to the “ visualization” of data. In construction, the finish of the house is irrelevant without the right engineering and architectural principles that are used in building the house.
In analytics, visualization is meaningless without the data foundation or to put it in construction terms without any “engineering” or “architecture” of the data. But the key in building this “data” foundation is the analytical file which is the core responsibility of the data scientist.
In today’s Big Data world, though, some organizations seek to identify problems through what is called a data discovery type approach. Instead of just using data to solve a specific problem or challenge, organizations also use data in identifying upfront problems.
As visualization technology has advanced, raw data such as web logfiles, transaction files,etc. can be analyzed without any data prep or manipulation done by the data scientist. For example, at a very basic level, Google Analytics provides nice graphical trends overtime to give insight on what days of the month yielded the most activity.
Additional drill-down can provide the type of pages that generated the most interest during this time. Yet, although Google continues to increase its dominance of analytics within the digital sphere, other tools are required when looking at non web data.
Even within our increasing digital and Big Data world, optimizing customer value is still a core mission of all organizations where CRM programs and analytics are the tools used to achieve this goal. Here the data needs to be worked in order to create that “one view “of the customer.
This requires that the data scientist manipulate and organize the information in order to present this view. However, before the data scientist even performs this kind of work, some type of “forensic” analytics can be done on the raw data. Why would this be done. Once again, it’s all about learning. Is there something inherent in the raw data that the analyst needs to understand before even creating this “one view” of the customer.
Many of the more advanced tools will allow the analyst to conduct statistical analysis to see if these results and findings are indeed statistically significant given the out of pattern view that is visually presented to the analyst. This initial view of the raw data could yield findings that the data scientist incorporates when building this one view of the customer.
In most cases, though, the analysis of data requires some level of customization or data manipulation with the ultimate deliverable being some kind of analytical file that will be used for visualization.
These pivot tables offer tremendous flexibility in allowing the user to create many different types of reports. The key, though, in building these reports , is to understand the basic concepts of business analysis. In other words, what are we measuring and how do we want to view these measurements(dimensions).
Identifying what is to be measured and how we might want to view these measures is critical to the creation of any pivot able. The use of columnar and database compression technologies by firms within the business intelligence industry has yielded better tools to facilitate the development of a given solution. The increased granularity in terms of measures and dimensions of these data exploration tools allows the analyst to look at a much wider variety of options in attempting to solve a given business problem.
Data Visualization is not a solution but simply one of the many tools within the analyst’s arsenal or toolkit. With the right data visualization tools, more thought can then be spent on solving the problem and what data do I need to use to potentially solve this problem and hence, solution providers working in this segment can create better data analytics solutions in this space.
Effective use of visualization is meaningless without the right data foundation.. Having the right data foundation, visualization can then be used to truly optimize the “story telling process” of analytics. This while segment of data analytics is still opening up and in future this will hold great promise for the overall growth of the industry.