Execs, Here is $100M You Already Possess But Are Unaware Of

In November of 2016, OECD held a meeting on Big data: Bringing competition policy to the digital era. The meeting identified that the use of big data by firms for the development of products, processes, and forms of organization has the potential to generate substantial efficiency and productivity gains. For instance, it can improving decision-making and forecasting, as well as allow for better consumer segmentation and targeting.

The meeting sought to develop a regulatory framework around the use of big data by multinational corporations to prevent a monopoly. You know there is a quite bit of money involved in a business model when regulators start to actively ponder and discuss it.

Big data and data analytics are multi-billion-dollar businesses whichever way you look at it. (Please refer to Facebook and Google’s revenue growth charts below.) Your company could actually be sitting on a multi-million-dollar treasure without you realizing it. This post is a quick overview on how to discover and utilize that treasure through digital transformation.

 

Source: https://www.statista.com/statistics/266249/advertising-revenue-of-google/

The Path to Discovering Powerful Insights

Every other technology article that you read these days is invariably on big data, cognitive analytics, artificial intelligence, data analytics, deep learning, etc. The value of data analytics lies in its promise to generate key insights on the customer segment your company is serving.

As per DataMeter, customer analytics, operational analytics, fraud and compliance management, new product and service innovation, and enterprise data warehouse optimization are the most popular use cases for data analytics. You can broadly classify these use cases into two categories: revenue generation and cost savings. Customer analytics and new product and service innovation help to achieve revenue growth, while operational analytics and fraud and compliance management help with cost savings. Enterprise data warehouse optimization can help in both categories.

You might wonder how this is different from all the data reports that have been generated for decades. While executives at insurance companies, financial services companies, and government agencies historically looked at reports generated based off legacy applications, typically they would be limited to transaction or customer data within an organizational unit. Advancements in data generation, storage, and analytics technology have made it possible to generate a high quality and quantity of data across multiple business units and in various formats.

Here is an example of data generated in various departmental silos at a financial services company:

 

If this data is collected, is available within organizational silos, and is scattered across multiple applications with their own data sources and formats, the chances of achieving a powerful insight in real time is pretty limited. To extract the true value of data, this information has to span across the organization and be processed in real time. E.g.: A customer’s banking transaction data should be made available to the wealth planning advisor to generate more cross selling revenue.

The Lean Startup principle advocates for organizations to build, measure, and learn in a continuous, rapid loop. This will help organizations receive quick feedback on their capabilities and respond to changing market conditions in an agile way.

Source: Lean Startup principle

The key to the “Measure” point above is the availability of useful data. In order to measure data effectively, organizations have to digitize their capabilities. Not only should they digitize, but they must do so in a strategic, coordinated manner known as a “digital transformation.” You might ask—“How is this different from enterprise modernization?” Read on...

Digital Transformation

In an article on designing the operating model in a digital world, the authors at consulting firm McKinsey & Company have identified the following five approaches:

Source: McKinsey & Company

There are various technologies and methodologies available to achieve the approaches and capabilities above. Out of these approaches, let us focus on the Lean Process Design, Digitization, Intelligent Process Automation, and Advanced Analytics.

Lean Process Design. Organizations need to think beyond functional silos. They have to keep the end-to-end customer journey and internal processes in mind. It is critical to make the processes smarter, leaner, and more efficient. There a need to identify end-to-end processes and eliminate bottlenecks. Steps that do not add value need to be identified and eliminated. Activities should be viewed through the lens of the digital business model to identify whether they still make sense.

Digitization. It’s critical to transform manual processes and activities into digital applications. With manual processes and activities, companies typically have a lot of information but very poor visibility and a limited possibility of generating analytics. If the information is not easily accessible and not available on demand, the chances of generating powerful insights are remote.

Data is valuable when it is available in real time. It is even more valuable when it is predictive. Like a popular quote in the data analytics community, the value of data is similar to the value of a old, current, and future newspaper.

Enterprises need to invest in digitizing their capabilities and offerings. It’s also critical to integrate applications via enterprise integration layers so that useful data from applications can be combined in real time to help with data-driven decision making. Organizations need to think of new digital channels where none exist today. The more of client's offline activities are converted to digital, the better data an organization has to drive powerful insights.

Smarter/Intelligent Process Automation. Identify patterns in processes—how many of them can be automated or processed straight through, and how many of them need manual intervention? Enterprise software leaders such as IBM offer comprehensive industry solutions and software that addresses these needs. By embedding cognitive capabilities into processes, it’s possible to identify patterns, centralize the decision-making logic, automate request processing, and gain real-time visibility into these processes. IBM Watson offers APIs that can be plugged in to process information such as brand sentiment through social media posts, for example.

Advanced Analytics and APIs. The World Economic Forum has likened data to oil, noting that data is a raw resource similar to crude oil that can be processed and applied in multiple contexts. The chief raw material for advanced analytics is availability of data. Like we mentioned earlier in the post, organizations have a ton of data, but much of it is buried in tons of paper or stuck within organizational silos. To effectively extract value from this data resource, it’s critical for organizations to expose functional unit data through APIs in real time. This data can be used by other applications to drive operations and combined with other data sources to drive useful insights.

Closing Comments

There is multi-million-dollar value to be extracted by exploiting the data that your organization already possesses. It’s critical to invest in the necessary digital infrastructure to effectively harness and extract this value. Digital transformation is the way forward. If you are looking for a trusted partner to accompany, strengthen, and advise you during your digital transformation, please get in touch with Prolifics. Prolifics is a premier IBM Business Partner with cutting-edge talent and technology expertise in digital transformation. Advisory consultants from Prolifics can conduct a workshop and help you draw a roadmap for your digital transformation.

 

N. R. Vijay
Adviser/Delivery Manager

N.R. Vijay is an Adviser/Delivery Manager in the Smarter Process Advisory division of Prolifics. He has over 13+ years of consulting experience across domains such as Retail, Healthcare and Banking. Specializing in technology, management concepts and enterprise strategy, he is focused on change management and process improvement initiatives. He co-authored a white paper titled "Improving Customer Loyalty through Business Process Optimization and Advanced Business Analytics"