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ON DEMAND WEBINARS
BI Analytics:
An engine for your
business strategy
Speakers
Juan Kanggrawan
Head of Data Analytics at
Jakarta Smart City
Seshadri Manivannan
Vice President | Chief Architect at
Tata Sky Ltd.
Kingsley Jones
Founding Partner/CIO at
Jevons Global
Summary

According to Forbes, more than 90% of the world’s data has been generated in the last 5 years and businesses have since prioritized on finding solutions that would assist them in gathering and converting this data into meaningful insights. To businesses, data insights have proven to be strategic in increasing their understanding of the forces shaping the markets, monitor how their customers are evolving, and help them stay ahead of their competitors.

A business’ ability to collect valuable information to drive their business strategy is at the heart of Business Intelligence. BI is not just about generating reports; it is an engine to make strategic and tactical business decisions, which directly influence the growth of your firm.

To understand how to effectively leverage BI solutions and stay ahead of the curve, join our panelists for an engaging discussion.

To know more about our Digital Transformation services

FAQs

How can BI and Analytics fuel business growth?

Strategy drives the value an organization creates for its customers. Be it through their products or services. And data has an immense role in defining this strategy and executing this journey. Business Intelligence helps companies learn what has worked and what hasn’t, and advanced analytics can further guide the future decisions of business proactively. BI & Analytics hold the key for empowering consumers/users make informed decisions.

What is the first-step for an organization towards enabling BI and analytics?

While there are short-cuts to enable BI and Analytics, the best path is to start by defining the Data strategy. Having a data strategy in place enables clarity among all the stakeholders and delivers greater return on investment. Establish the business objectives like improve customer satisfaction, expand to new markets, increase revenue, etc. and work backwards to enable solutions that deliver these objectives.

What is the BI and analytics maturity model?

There are many BI and analytics maturity models that one may come across. Most of them will assess the extent to which an organization is Descriptive, Diagnostic, Predictive, and Prescriptive aspects of data.

  • Descriptive: What happened?
  • Diagnostic: Why did it happen?
  • Predictive: What will likely happen?
  • Prescriptive: How can we make it happen?
What is the role of data in BI and analytics efforts?

Data is an asset, which when managed and leveraged well, can drive organization growth. BI & Analytics platforms enable an organization to harness data and its power to drive business. It is important to have a good data foundation for faster and better BI & Analytics outcomes.

How can a company ensure that everyone speaks a single version of the truth?

This is possible when all three dimensions of people, culture and processes come together. Everyone in the organization must align on the metrics, dimensions and definitions and then work towards common goals. And a single source of truth, the foundational layer, is key to making this happen.

How can one choose the right technology options in the BI and analytics space?

There is no one-size fits all solution in the market. Choosing the right option will start with asking the right questions to the right people. While the wish-list can be endless, it is important to prioritize the expected functionality from the platform and work towards identifying options that best meet those priorities. And the technology options could be commercial or open-source or even custom-built.

What is the best way to yield maximum ROI on BI and analytics investments?

For yielding better ROI in BI and Analytics, start at the end goal. Have business needs captured and prioritized. Ensure there is alignment on these priorities across the organization. Have an impact vs. efforts matrix defined for all prioritized needs, and assess the time and resources that will be required for each line item. Remember to have a feedback loop in place to iterate and deliver outcomes that can be better adopted.

What are the common pitfalls with BI and analytics efforts?

Few common pitfalls to quote are –

  • Lack of a well-defined problem statement
  • No support from stakeholders
  • Lack of governance
  • No agility and feedback loop in place
  • Gaps in tools/technologies
How well can one trust the out-of-box insights offered by new-age tools?

While new age tools offer out-of-box insights, they still have a long way to go before they can be blindly trusted. Real world problems, especially when data is involved, are complex to complex. And the insights offered through tools have to be in a consumable format too. For these insights to be trusted, consistency in the definition of KPIs and a single-source-of-truth would be critical.

Are self-service capabilities in BI and analytics a reality?

Yes, new-age tools are offering self-service capabilities. A point to note with enabling this feature is to have good governance in place. The usual perception of governance is that there will be a lot of control, approval processes, etc. and it is therefore important to balance business empowerment and IT controls.

How can a company venture into advanced analytics spaces like predictive, artificial intelligence/machine learning?

It all begins with defining the business purpose. And then having the right team and solution approach. Couple of points to call out are the experimental nature and the iterative efforts required to deliver better outcomes in predictive analytics and AI. Competency also plays a key role in ensuring that advanced analytics efforts don’t end at a Proof-of-Concept stage but are indeed deployed to production, providing sustainable business value.

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