What is digital transformation?

On a single process basis, digital transformation involves using digital technologies such as Cognitive Automation to optimize a process. The idea is not to replicate an existing service in a digital form, but rather reimagine that service into something significantly better. 

At the enterprise-level digital transformation is the integration of digital technology into all areas of the business (people, process, technology), fundamentally changing operations and the delivery of value to customers.

For most organizations, digital transformation is about survival. While back in 1958, the average lifespan of an S&P 500 company was 61 years, in 2020, it’s more like 18 years. In order to remain competitive in an increasingly chaotic market, companies are turning towards digital transformation to:

• Decrease operational costs by increasing productivity, efficiency and reducing FTE
• Enhance and evolve the customer experience (i.e. respond to CS requests faster, develop personalized product recommendations, build better customer facing interfaces, etc.)
• Optimize the employee experience by reducing administrative burden 
• Develop new services, products and revenue streams
• Improve data collection, visualization and analytics capabilities
• Enable increased organizational agility and resilience  
• Support the implementation of AI-powered tools both from cultural and IT infrastructructure standpoints 

 

*Video sourced from "How six companies are using technology and data to transform themselves," https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/how-six-companies-are-using-technology-and-data-to-transform-themselves 

 

Digital Transformation is about People; not just Technology

In an environment where digital transformation is a requirement, not an option- the fact that less than 30% of these initiatives succeed is alarming. However, that success rate is not entirely new.

Since the 1990’s, the success rate for new IT implementations has stagnated around 25%, meaning 75% of IT projects either failed to deliver results, went significantly over budget or took too long. For data and analytics initiatives, the failure rate is even higher (around 85%).

Given all of the benefits above, how is that much unuseful failure possible? Though, in a lot of ways, the answer will vary from company to company- effective change management (or lack thereof) is almost always a key issue.

Digital transformation is not just about digitizing processes, more than anything else- it represents a “cultural change that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure.”  

In 2018, McKinsey released its 21 Keys to [Digital Transformation] Success. Each and every key was talent and/or change management related. In a separate study, Deloitte also found that organizations that achieve digital transformation success have 4 things in common:

• They create and communicate a clear, coherent digital strategy that integrates with overall corporate strategy
• They inspire and prepare their workforce to use digital technology to its full potential
• They prioritize cultivating a culture that is nimble, collaborative an open to calculated risk
• They focus on talent and realize that people, more than technology, will enable long-term success

Successful digital transformation strategies are built around how human behavior and technology intersect and how one can augment the other. Business intelligence provides organizations with a tangible, numbers-driven way to tell that story.

 

READ NEXT: Advanced Analytics: How Next-Level Insights Are Shaping the Future of Business

What is business intelligence?

Business intelligence (BI) refers to the strategies and technologies used to transform raw data into meaningful, actionable insights. Though, historically speaking, BI really only referred to the production and use of historical, descriptive operational data, that definition has changed. As Tableau puts it, “Business intelligence (BI) combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations to make more data-driven decisions.”

While enabling technologies such business process automation (BPA) tools or enterprise resource planning (ERP) platforms may be the vehicle that drives digital transformation, data or business intelligence is the fuel. The primary goal of BI is to help companies bring order to the massive amounts of data they collect by supporting these 4 basic objectives:

Data collection. If nothing else, BI tools quickly and efficiently collect pre-determined sets of data from enterprise systems such as CRMs, financial management platforms and HRMs. In addition, RPA and AI-powered bots can scrape website, spreadsheets and other documents for additional data, both structured and unstructured.  

Data storage.  Data lakes and data warehouses serve as central source for both structured and unstructured data. These tools safely and securely store large amounts of data for later use in processing and analysis. They also cleanse, integrate and organize data so that it’s high quality and easily accessed by data reporting and analysis systems and ultimately used by subject matter experts within the business.

Data analysis. The core of business intelligence is focused on descriptive and diagnostic analytics, which answers questions of where your company has been, where it is now, and why things are the way they are now. BI tools need to be able to draw from data storage to conduct these different types of analyses.

Data reporting. In order to be useful, data-driven insights must be easily and reliably accessed by decision makers and other stakeholders. Instead of waiting for the IT department to create a report based on business data, self-service reporting tools allow business users to create visually engaging reports themselves.

BI and digital transformation are deeply intertwined. In fact, once could say BI is both the enabler and result of digital transformation. As Rahul Singh, managing director of IT and business services transformation advisory firm Pace Harmon, explained to the Enterprisers Project, “the ability to analyze vast amounts of structured and unstructured data to gain insights, often in real time, is what underpins most digital transformation efforts, as the insight derived through big data analytics is used to drive digitization and automation of workflows.”

However, companies that only use data to guide and track digital transformation efforts will miss out on a goldmine of strategic insights. While business intelligence may be the fuel that drives digital transformation efforts forward, digital transformation should advance business intelligence and analytics capabilities as well. 

 

*Image sourced from "Business Intelligence: The Leap You Need," https://medium.com/@mperceptacademy/business-intelligence-the-leap-you-need-58b2742fd186 

 

AI-Powered Business Intelligence

For decades, BI could only tell users “what is happening” or “what happened,” leaving the decision-making part to humans. However, with the emergence of artificial intelligence (AI), that is rapidly changing. Now, using data mining, machine learning, prescriptive analytics and other innovative technologies, organizations can use BI to uncover and share new, groundbreaking data-driven insights.

By modeling human behaviors and thought-processes, AI programs can learn and make rational decisions. AI can enable BI tools to produce clear, useful insights from the data they analyze and help companies more easily synthesize vast quantities of data into a coherent action plan.

According to The Value of AI-Powered Business Intelligence, the confluence of AI and BI will permanently change the way businesses operate in 3 fundamental ways:

• Data Democratization: line-of-business users will be able to easily discover and understand data-driven insights without any sort of technical or data science training
• Next Generation Natural Language Processing (NLP): capable of understanding and using natural language prompts, NLP learns from users’ interactions to customize and personalize insights 
• Automated data cleansing and preparation: The system does the tedious work of preparing data for analysis, freeing up IT analysts and LOB users to engage in more productive work 

 

Predictive & Prescriptive Business Intelligence

Predictive analytics is the use of data, statistical algorithms and machine learning (or AI) techniques to identify the likelihood of future outcomes based on historical data. As the experts at IBM put it, “the use of predictive analytics is a key milestone on your analytics journey — a point of confluence where classical statistical analysis meets the new world of artificial intelligence (AI).”

Using data mining techniques, predictive analytics platforms sort through massive amounts of not structured and unstructured data to a.) identify patterns and b.) make predictions based on past behavior. 

Organizations of all kinds are leveraging predictive analytics to drive value in a myriad of ways. For example, IT departments might leverage predictive analytics to predict storage usage. Predict analytics can also dramatically improve supply chain resiliency by enhancing demand forecasting, inventory planning & last mile delivery.  Even hospitals are using predictive analytics to predict patient outcomes and help doctors make more informed treatment decisions as well as optimize resource planning.

Prescriptive analytics take things one step further. In addition to leveraging predictive analytics to forecast what will likely happen, but also recommends a course of action. By combining predictive analytics with another layer of AI, prescriptive tools can simulate various outcomes from worst case to best case and show the probability of each. 

Though not always 100% accurate, they help organizations make decisions based on facts and probability-weighted projections. For example, by combining standard process mining techniques with prescriptive analytics, a Recommendation-Based Business Process Optimization systems could map out, test and rank potential new processes.

 

In Summary….

Business Intelligence (BI) is a powerful enabler of digital transformation before, during & “after.” While BI helps organizations prepare, plan and build a business case for digital transformation as well as track its progress, its true value comes after process digitization.

As Everest Group’s Ronak Doshi puts it, “The business value (or return on investment) that an enterprise can generate from their investments in the digital capability platform will depend on their data value extraction capabilities.”

If fully optimized post-digital transformation, advanced BI systems can provide businesses with continuous, actionable intelligence on what comes next. Whether that be the development of new service offerings, the implementation of new IT projects or new ways of doing business, transformational BI can guide the way.