31st Jul, 2018
Denave, Team, Designation
‘Intelligence’ doesn’t go out of fashion! Gartner’s yearly technological trends underline this further. The list mentions ‘intelligence’ right on the top, for ten years in running! What exactly does ‘intelligence’ entail – in this increasingly integrated digital world? Is it the ability to make the right ‘strategic decisions’? Decision-making process is backed by experience, intuition, and relevant data.
While the value of intuitions and experience that business leaders bring in, cannot be negated, its availability and accessibility are unfortunately limited. Thanks to the digitalization wave, we have constantly increasing petabytes of free data and a variety of information at our finger’s tip. To draw meaningful business inferences from this perpetually increasing data reservoir is, therefore, a persistent analytic challenge – one which is the essence of digital transformation.
Data mining, data structuring, predictive analytics in this context are not very new concepts. Applications pertaining to fraud detection, risk analysis, customer segmentation, sales forecasting, financial modeling etc have been using Analytics for some time now.
The need has been felt to build operational efficiency while undertaking the digital transformation. This is the essence of Business Intelligence (BI) – adopting a new approach while implementing the existing processes. This makes the entire decision-making process more agile, more responsive and more intelligent. Towards this goal, analytics need to be an integral part of BI solutions rather than a stand-alone application.
Data and Doubt
The journey is towards the use of data to enhance and improve the effectiveness of decisions taken by every stakeholder. Data is being generated in enormous volume from disparate mediums and formats.
So, there is no dearth of data at hand but what is lacking across organizations are the tools to sieve out text analytics and sentiment analysis from this unstructured data. Applications working in silos are another hurdle as it generates data silos that require a good data specialist to put this big data in order.
Data hierarchy in correlation to its accessibility by business units or data stratification in terms of experimental data sets or vetted data sets to be used by an entire enterprise is still in nascent stages. This has a direct impact on data quality at disposal, effective data curation and tools and technologies to cull out in-depth insights via data discovery.
BI dashboards encompassing key performance indicators – a form of data – can either support in decision-making or can help automate decision-making. While the BI dashboards will continue to vary in terms of design, interface, functionalities, and capabilities, it is the data management which rests at its core. Data Management plays a key role in the Data Analytics strategy. Lack of skilled data analytics is the basic grouse towards robust data management.
The Leap forward
Enterprises globally agree that the ability to solve the massive data complexity, in real-time is the only differentiator that can elicit cutting-edge advantage in an ever-changing business environment. New age solutions and capabilities are oriented towards interpreting the full spectrum of unstructured data.
Some of the widely anticipated movements are listed below.
Data discovery – movement from visualization to narratives
Data visualization is conceived with the intent of providing key information in a compact and comprehensible format. However, if only a handful of data analysts can decipher the story behind the impressive graphics, then data visualization concept loses its purpose.
It is important that a thing of beauty is coupled with brains – a mere aesthetic value of data visualization without the clear functionality of providing comprehensible communication enabling in-depth insight in an intuitive way is proving to be a major gap area.
Future of BI tools – Accessibility, Self-service, collaborative and security
The perception and process that BI is basically a data warehouse that only limited IT experts can access shall break down. IT department’s role would increasingly orient towards the maintenance of IT structure.
The next level of BI tools will enable users to access data, query the data, gather intelligence, analyze reports and deduce actionable insights. All this without depending on IT experts for permission or much of their support. It would also be immaterial if the users are or not from Data Science or Data mining background.
The future of flexibility of accessibility and self-service enabling BI tools is tied up with the evolving dashboards. Since the focus is on users who are not necessarily tech-savvy, it is crucial that the BI tools are navigation friendly and the dashboards are user-friendly. It will also bring in collaborative Business Intelligence to reach the desired outcome.
While the engineered accessibility would be basis user and the related role, this shall bring security parameter, yet again, at the forefront, considering the criticality and proprietary of information. It will be imperative to verify data security controls before deploying self-service BI solutions.
Data structuring – Simpler and Scientific
Sources pertaining to data capture shall continue to grow but will get more streamlined with the growth of IoT and speech recognition. The data becomes meaningful only when it is analyzed. Mere collection of data from increasingly diverse sources, however, streamlined doesn’t help.
Hence, Data structuring shall continue to be a very important aspect of BI solutions. Capabilities are being enhanced to interpret the full spectrum of unstructured data. Machine learning has made it possible to structure the unstructured data formats into more decipherable formats which earlier would be a hurdle to scalability.
Snehashish is of the view that adds “Companies investing in making it simpler and scientific shall rule in the BI platform space.”
Sunil Munshi, CEO, Denave India, opines that “Data structuring shall be more scientific giving better actionable inputs to the BI-Analytics engine.”
Prescriptive Analytics – prescribing future
Analytics – Descriptive, Predictive and now Prescriptive– only serve to give holistic view to enterprises via data aggregation and data mining, using stats to forecast the understanding of future and advice on possible outcomes.
Predictive analytics based on historical data that cuts across the organisation churns out estimates for prescriptive Analytics. Prescriptive Analytics gives number of ‘prescriptions’ i.e. possible outcomes of various decisions, if taken. By its sheer concept, it is a complex engine and right now exists under the purview of only big companies.
Bed of roses
Analytics-driven BI is undoubtedly increasingly being employed to support corporate decision-making and weighs heavily on the strategy aspect, with only 20% of businesses still to jump into the fray. What is noteworthy is that many of the organizations have only rudimentary analytical technology in place.
Ninety-six percent of leaders feel that Analytics has bright future ahead and shall become more important in the next three years.
While this might sound music to Analytics and BI platform solution providers they need to understand the key barriers to the technology adoption. Data management, right skills and positive approach towards change management are more difficult to embrace than technology itself.
So, it is not all bed of roses! It doesn’t matter till when ‘Intelligence’ remains in fashion. The important aspect is making it mainstream. This can be possible only if it is tempered with widespread adoption, aligning the organisation to the possibilities and bringing in the culture of data-driven decision-making.