Introduction to Advanced Analytics

Category
Analytics
Author
Sandeep Keloth

Nowadays, every business unit regardless of their size and sector, they have all realized that there is enormous potential for improvements in efficiency and new revenue opportunities are hidden in their data using Big Data and Advanced Analytics

But after the initial stages of breakthrough and proof-of-concept test runs, the time has come to look at the bigger picture: enterprises are now thinking about the structures that they need to put in place to keep moving forward and make the most of their big data on a permanent basis.

The payoff from joining the big-data and advanced-analytics revolution is no longer in doubt. The tally of successful case studies continues to build, reinforcing broader research suggesting that when companies inject data and analytics deep into their operations, they can deliver productivity and profit gains that are 5 to 6 percent higher than those of the competition. The promised land of new data-driven businesses, greater transparency into how operations work, better predictions, and faster testing is alluring indeed.

But that doesn’t make it any easier to get from here to there. The required investment, measured both in money and management commitment, can be large. CIOs stress the need to remake data architectures and applications totally. Outside vendors hawk the power of black-box models to crunch through unstructured data in search of cause-and-effect relationships. Business managers scratch their heads—while insisting that they must know, upfront, the payoff from the spending and from the potentially disruptive organizational changes.

What’s clear is that Big Data and Advanced Analytics are here to stay. Despite apparently recent arrival, it is just another step on a path that began long time ago with Business Intelligence (BI) and aims to continually improve how the enterprise operates by utilizing the available information. Business Intelligence, with its dashboards and reports, shed light on the past but left it to human intuition when it came to drawing conclusions about the future. With Advanced Analytics and JKT’s PPF data approach, the science of prediction has superseded mere description, with the ability to identify data patterns, social media sentiments, weak signals and hidden explanations. And as systems become ever more powerful and increase the information knowledge, the need for prescriptive analytics is emerging, which will provide direct assistance and practical decision support, as well as cognitive analytics, which will enable even more advanced automation. This is not only a technological roadmap, but it is also about how organizations are becoming increasingly mature in their ability to organize themselves around data. So, although it may be only at the beginning, it’s obvious that those who advance the fastest will reap a significant competitive advantage.

Nevertheless, feedback from early Big Data & Advanced Analytics projects already hints at the challenges looming on the horizon which call for a flexible framework to allow agile business implementation. Firstly, the constantly changing nature of data (be it in volumes, in diversity from different sources, and Velocity) as well as a growing demand for real-time analytics capabilities. Secondly, the relative lack of expert resources, with good Data Scientists having long been a rare and expensive commodity in the face of increasing demands. And finally, the already noticeable risk of silos between use case implementation reappearing and the resulting inefficiencies.

In the same way that digital technology calls for specific organizational and operational provisions (Cloud, DevOps, etc.), Big Data analytics requires appropriate responses to anticipate and overcome these pitfalls and ensure that investments in it are sustainable. That means rapidly establishing an industrialized approach, that draws together an organization’s initial Big Data projects to optimize cost control, rationalize resources and capitalize on experience.

To achieve this without restricting the room for maneuver that is essential to the business, the solution lies in the orchestration and close co-ordination of four dimensions which make the Advanced Analytics Solution:

  • Firstly, upstream, consulting teams that can engage with the business to rapidly identify and qualify effective business use cases using proven methodologies.
  • Then comes the ‘Design Labs’: teams and tools that enable organizations to rapidly design, qualify and calibrate prototypes to validate their use cases.
  • After that, it’s time to go into production with the rapid implementation of a multi-purpose analytical platform that is compatible with various computing environments, including private or public clouds, and is responsible for collecting, formatting, processing and presenting data (managed by the IT Department, it is the foundation stone of industrialization). This is where Big Data Platform (BDaaP) comes into play.
  • Finally, the underlying infrastructure, which provides the necessary performance (even up to High-Performance Computing levels if the amount of data or complexity require it), underpins the effective operation of the source systems and guarantees security.

JKT uses this philosophy in its Advanced Analytics solutions, to ensure that Big Data and Advanced Analytics is not just a series of disjointed initiatives within an organization, but rather a sustainable and concerted strategy for value creation. Founded on the belief that Big Data & Advanced Analytics supports a long-term strategy, this approach is not only facilitating today’s projects – by promoting the emergence and implementation of use cases – but also those of tomorrow, creating the conditions for industrialization and future capitalization. By using JKT expertize in Big Data and Advanced Analytics, the enterprise can make a genuine long-term investment in Big Data while also speeding up its route to optimum exploitation of all the data it can access.

The next Step, is to develop a plan. Literally. It may sound obvious, but in our experience, the missing step for most companies is spending the time required to create a simple plan for how data, analytics, frontline tools, and people come together to create business value. The power of a plan is that it provides a common language allowing senior executives, technology professionals, data scientists, and managers to discuss where the greatest returns will come from and, more important, to select the two or three places to get started. The detailed narration of the plan development will be continued in the next sequel of this series of post

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