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A Traveler’s Guide to Digital Transformation

Intellicus > A Traveler’s Guide to Digital Transformation

A Traveler’s Guide to Digital Transformation

Rajesh Murthy, Vice President at Intellicus technologies, founding architect of Intellicus BI software product. He led this spin off from Impetus technologies in 2004, which has gone on to acquire more than 17,000 customers. He has initiated patented innovative techniques in the area of data processing and visualization for Intellicus. He strategizes business growth and works with clients to deliver transformative scalable solutions and ensures project success. He oversees all key functions at Intellicus and is closely involved in product design. Rajesh also serves as VP of Engineering for Kyvos Insights, a BI accelerator layer for big data BI stacks. Rajesh started as a Database Administrator and then as DB technologies coach at Tata Unisys Ltd. He has designed data models and written data programs for SBI, UTI, RPG, to name a few in India and then during his Impetus tenure, went on to create high volume data processing and reporting layers for 3M and Walgreens. Rajesh graduated from UCVE, Bangalore with an Engineering degree in Electronics. He loves traveling by road and does some organic farming in his leisure time.

As organizations attain maturity in their digital transformation journeys, insights from business intelligence and analytics are accelerators for growth that business leaders cannot afford to ignore. To make the most of the power of data, it is important that they recognize the five milestones in the journey.

1: Stand-Alone Systems

Digitization is nascent at level 1, possibly with only stand-alone Point of Sale (POS) machines with a billing-cum-inventory software. However, by shifting from handling transactions manually to using a computer to manage their data, an organization has already taken the single most important step in their digitization journey.

Capturing transactional data on a computer is the foundation on which every other level of data maturity rests.

At this level, reports are business critical, but do not offer much insight or decision support. The organization’s need for IT support for routine reports is very low, but their dependence for any customization is absolute and ad-hoc reports simply aren’t possible.

2: Enterprise Resource Planning

As businesses grow, organizations quickly identify the need and benefits of seeing data from across the whole business. Investing in specialized solutions for key departments helps reap benefits from enterprise-wide data, improved customer experience and economies of scale. By adopting ERP solutions for all their departments, organizations reach data maturity level 2.

A majority of organizations—SMEs or large enterprises—are at data maturity level 2.

At level 2, each of the systems produces a wealth of data on key indicators and decision making has started becoming data-driven. However, reporting has a massive dependency on IT, even for routine reports. It takes collaboration among data administrators, programmers, report designers and testers to produce reports. However, obtaining a new report can take between 30 to 60 days .

3: Data Warehousing

Despite having a world of knowledge about each individual function in their organization, level 2 organizations can face challenges in getting to the root of business problems. Data and ‘prepared reports’ from different departments are rife with inconsistencies and conflicts.

The answer to this challenge lies in putting all data into a data warehouse (DWH), the end product of a process called ‘data integration’ which integrates the data pipeline of  an organization with the help of ETL. Data integration experts work with all the departments to integrate all internal as well as external data sources to create a single dashboard. With the creation of a DWH, an organization arrives at data maturity level 3.

Data warehousing helps business leaders in breaking silos to get a unified 360-degree view across all functions of their organization.

The DWH has another paradigm altering feature – a ‘universal semantic layer (USL)’, also called an ‘enterprise semantic layer‘, or a ‘meta layer’. USL allows any business user to design, customize or drill-up or drill-down reports—all in the matter of a few minutes and without ever having to raise an IT ticket.

Armed with the deep insights discovered through a unified source of data, business leaders now have the capability of answering questions like “Was customer retention impacted by marketing campaigns? Does the customer’s online activity inform their in-store behavior?”

4: Data Science, AI & Machine Learning

Organizations can tap into the power of their historical data through DWH. However, all innovations in business and operational models have to be tested on the field, a costly and slow affair. Some shifts are so fundamental that the associated risks made any steps in that direction impossible.

Level 3 organizations can ramp up their predictive analytics and data modeling capabilities to solve this problem with what-if analysis, which allows testing  models digitally.

Data modeling capabilities help an organization in moving from reports to actionable insights.

Data modeling with what-if analysis helps in simulating changes in one or more factors that affect a business. A level 4 organization are capable of answering questions like “Are we getting the most value from our supply chain? How do we predict customer behavior and preferences? What will be the impact of reducing the risks associated with a global supply chain?”

5: Data Monetization

An organization’s investment in their data is now ready to bear yet another fruit—monetization. A global enterprise’s presence across multiple continents yields insights into multiple domains, markets, supply chains and consumer preferences that can be used by other businesses . Self-serve analytics has enabled a shift in IT focus, from internal reporting to monetizing the assets they have helped build over the years. With this pivot, a level 5 organization is tapping into revenue streams that their competitors aren’t considering yet.

With data monetization, IT and data resources built over the years move from being a cost center to a profit center.

Monetization is done in two ways – by monetizing analytics and insights , or by monetizing ML data models and predictive capabilities. A level 5 organization’s business partners benefit equally from the insights discovered from their data-first culture.

While most business leaders appreciate the benefits of reaching the next level of data maturity, several obstacles lie in the path of attaining that level. Technical and software complexities are often the most common impedances in the path of data maturity. Intellicus offers not just an analytics and BI platform, but also professional services which deliver ‘Analytics and BI as a Service’.

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