How Business Intelligence (BI) Drives Digital Transformation
Digital transformation is what it’s all about.
Based on a single-process basis digital transformation is the use of digital technology like cognitive Automation to enhance the process. The goal is not to duplicate an existing process in a digital format, but rather to reinvent that service to make it substantially superior.
At the level of the enterprise, Digital transformation involves the integration of technology in the various areas of business (people processes, people, technology) fundamentally altering the way in which operations are conducted and the value that is provided to the customers.
For most organizations, digital transformation is about survival. In 1958, the life span for an S&P 500 business was 61 years old In 2020 it’s more than the average of 18 years. To remain relevant in an ever-changing market, businesses are turning to digital transformation to:
Reduce operational expenses through increasing efficiency, productivity and cutting down on FTE
Improve and enhance the user experience (i.e. be able to respond faster to CS requests faster, design individual product recommendations, design more user-friendly interfaces for customers and more.)
Improve the experience of employees by cutting down on the administrative burden
Create new products, services, and revenue streams
Enhance the collection of data, visualization and analytics capabilities
Increase flexibility and adaptability of your organization.
Facilitate the use of AI-powered tools from both perspectives of IT infrastructure and culture
Digital Transformation is all about People, not only Technology
In a world in which digitization is an absolute must and not an option- the truth is that just 30 percent of these initiatives are successful is alarming. But, this rate of success isn’t new.
Since the ’90s, the success rate of new IT initiatives has remained at around 25 percent and that means 75% of IT initiatives were either unsuccessful in delivering results, exceeded budget, or took too long. In the case of analytics and data initiatives, The failure rate is higher ( around 85%).
With all the advantages discussed above, how can so much ineffective loss be possible? However, in a number of different ways the answer can differ from one organization to another effective change management (or ineffective change management) is often an important issue.
Digital transformation isn’t just about digitizing processes, but more than anything else, it is the ” cultural change that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure.”
In the year 2018 McKinsey published the 21 key points to [Digital Transformation success. Every single one of the keys was change management and/or talent connected. In a different study, Deloitte also discovered that companies that have achieved digital transformation success have four factors that are common to them:
They develop and share an organized, clear digital strategy that is integrated with corporate strategy overall.
They encourage and train their workforce for using the digital age to their maximum potential
Focus on the creation of a culture that’s flexible, cooperative and open to taking calculated risks
They concentrate on their talents and understand that people above all technology can make a difference in long-term success
Digital transformation strategies that work are based on how technology and human behavior intersect and how they can complement each other. Business intelligence can provide organizations with an observable, data-driven method to tell their story.
AI-Powered Business Intelligence
For a long time, BI could only inform users “what is happening” or “what happened,” leaving the decision-making process to humans. With the rise technology like artificial intelligence (AI) this is quickly changing. Today, with information mining machine learning, analytical prescriptive techniques and other cutting-edge technology, companies can utilize BI to gain access to and share cutting-edge, new data-driven insights.
By modeling human behaviours and thought processes AI programs are able to learn and make logical decisions. AI can allow BI tools to provide insightful, practical information from the sources they study and assist companies to combine huge amounts of data into a unified action strategy.
According to The Benefits of AI-Powered Business Intelligence the convergence with AI as well as BI will forever alter the way companies operate in three major ways:
Data democratization Line-of-business users will be able to find and comprehend insights based on data without the need of data science or technical instruction
“Next Generation NLP (NLP) Capable of understanding and responding to natural language prompts NLP learns from user interactions to tailor and personalize information.
Automated cleansing and data preparation: The system performs the laborious task of creating data to be analysed which frees the IT experts or LOB users to do more productive tasks.
Predictive & Prescriptive Business Intelligence
Predictive analytics is the application of statistical algorithms, data as well as machine-learning (or AI) techniques to determine the probability of outcomes in the future based on previous data. According to the experts at IBM explain, “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).”
Utilizing techniques for data mining predictive analytics platforms comb through huge amounts of unstructured and unstructured data in order to.) find patterns and.) predict future behavior based on past behaviour.
Companies of all sizes utilize predictive analytics to increase results in many different ways. For instance, IT departments might leverage predictive analytics to forecast the storage requiremens. Predict analytics also have the potential to significantly enhance supply chain resilience through improving the ability to forecast demand inventory planning, forecasting and final mile deliveries. Many hospitals use predictive analytics to forecast patient outcomes and allow doctors to make better informed decisions about treatment and also optimize planning for resources.
Prescriptive analytics go an extra step. Apart from utilizing predictive analytics to predict the likely outcome and what will happen, it also suggests an action plan. Combining predictive analytics and the other layer of AI the tools of prescriptive analysis can create a simulation of various outcomes, from worst to best scenario and provide the probabilities of each.
While they aren’t always accurate, they can assist businesses in making well-informed decisions based on facts and probabilities-weighted estimates. The Recommendation-Based Business Process Optimizing system, for example, may map out possible processes and test and rate novel ways using traditional process mining approaches and prescriptive analytics.
Business intelligence (BI) is an effective catalyst for digital transformation, both before and during, as well as “after.” While BI can help organizations plan, prepare and develop a business plan for digital transformation, as well as monitor its performance, its true benefit comes from process digitization.
If properly optimized post-digital transformation advanced BI systems are able to provide companies with constant, actionable information regarding what’s coming next. It could be the creation of new offerings for services as well as the development of new IT initiatives or innovative ways of conducting business, transformative BI systems can help guide the way.