The Value of Analytics in Digital Transformation

Digital Transformation has become a board-level initiative across many organizations. It has culminated into the largest collaborative industry direction, stemming from the realization that most organizations have reached a point wherein they are unable to determine or gain substantial return on technology investments. With digital transformation initiatives spend in the Middle East, Turkey & Africa (META) region expected to reach $38 billion by 2021 according to IDC, and combined with the threat posed by next generation of digitally-savvy competitors, investments in digital business models have been propelled to new heights. A crucial part of this investment is being focused on advanced analytics.

The journey of digital transformation requires regional organizations to challenge the status quo by asking new questions. Advanced analytics helps organizations to generate new questions, and also find answers. IT had long assumed a central position across all business functions and operations, helping organizations improve processes, better manage risks, and deliver enhanced products and services to stay relevant. This still holds true. However, the impact of IT has elevated to a whole new level when an organization introduces advanced analytics and amplifies its focus on data.

Decision-making processes, which were implicit in the past, are now being assessed through advanced analytics, processing high volumes of diverse data to generate answers to frequently asked questions. When is a favorable time to launch our new product? What adjustment should we consider to our go-to-market strategy? What’s the likelihood that this particular price point will lead to growth in revenue? Will an offer get a better response on a Tuesday or on a Thursday? As part of their digital transformation initiative, these are few questions decision makers at different levels of an organization can expect an answer to, through intelligent analysis of data.

Imagine advanced analytics as an engine, accelerating an organization’s digital transformation initiatives with data-driven decision-making. Often, terms such as machine learning, big data, and analytics divert attention from the fact that these technologies and processes are implemented primarily to support and automate decision-making. Today, the role of advanced analytics in digital transformation has evolved, which means this metaphorical engine is taking flight using new type of fuel, i.e. big data helps organizations reach new heights – rather than simply driving faster – using the same fuel (data).

Advanced analytics completes the feedback loop between IT assets and business strategy, developing a capability that is so significant to operations that it ultimately becomes the business model. For example, farmers and agricultural companies are turning to analytics to increase production capabilities by placing sensors in fields to gain insights on topography of certain areas. Manufacturing companies have started using advanced analytics to process data generated by sensors, which can be ultimately leveraged to manufacture faster, smarter, and greener, helping to optimize the manufacturing process across the value chain.  Insurance companies are moving away from their age-old hardware and software set-up, embracing analytics-driven systems to automate processes, target new market segments and demographics, and reduce fraud.

Today’s advanced analytics, especially in the cloud, are more enhanced than their predecessors. The fact remains that this capability doesn’t exempt business and IT from coming together and understanding the core rationale behind introducing analytics, which will help them achieve their business objective.  Overall, we are seeing many organizations recognize the value of analytics and understand that data alone does not generate insights. It is the analytics derived from big data that creates true value. 

Organizations need to invest, innovate, and improvise big data to drive the value of analytics. Not every organization is successful in capitalizing on this newfound capability. Many organizations leap ahead and invest in new technology without thoroughly questioning its relevance to their business goals and needs.

There are three primary roadblocks faced by organizations involved in analytics projects:

Data integrity challenges:

Analytics projects are often unsuccessful because of incomplete or poor quality data. Although data integrity has been the topmost priority for any organization engaged in an analytics project, there is now the additional complexity associated with the migration of data and analytics to the cloud. Data governance, metadata, data integration, and related processes will need more attention than before. Questions like how to reduce the movement of data and how to bring analytics to the data will ascertain the capability of an organization to effectively manage an advanced analytics platform that enables and supports digital transformation initiatives.

Culture of data enablement:

In order to leverage the value gained from advanced analytics and make it pervasive across the enterprise, organizations needs to make a cultural change in the way they approach advanced analytics. To ensure optimal business impact, organizations must make sure every person is data-literate and values fact-based decisions. This means extending the reach of your analysis and insights beyond the borders of your organization and engaging the broader ecosystem, which includes the customers.

Emergence of the Chief Analytics Officer:

An organization with the right skills and talent enjoys a competitive advantage. Building on this, organizations need to consider new leadership roles such as a Chief Analytics Officer (CAO), whose primary responsibility is to ensure that analytics is uncovering value-added insights for enterprise-wide decision-making processes, and aligning the use of data assets to that of the business.

Having said this, organizations that have adopted some of these best practices have only scratched the surface of the potential benefits offered by advanced analytics. A few have leveraged predictive analytics to its full potential, fewer still are implementing optimization methods, and few early adopters have started embracing artificial intelligence for decision-making and process automation. However, we expect this area of focus to gain momentum as mainstay industries such as retail, banking & finance, manufacturing, telecommunications, and governments adopt new analytics technologies and platforms.

Overall, decision-makers need to remember that organizations that consider the importance of advanced analytics will be able to discover and deploy new business models, and potentially emerge as disruptors in their sectors. They will lead the market by gaining optimum benefits from their data, now and in the future.

Advanced analytics, when embedded in each transaction, interaction, information flow, and process, will drive the next wave of productivity and growth.