As of late, there are many organizations which are investing largely in Artificial Intelligence and Data Science, etc. The practices of AI, analytics, machine learning, and data sciences may vary, but the objective of all these and the goals are the same across an organizations, i.e., to increase the business efficiency, explore more market opportunities, and get more returns. It is understood that to stay competitive in the modern digital economy, organizations need to strengthen their products and internal processes. Now, this smart approach is made through tools and technologies related to data and artificial intelligence.
Based on user experience, the best mode of assessing the data and artificial intelligence maturity of an organization is by focusing on what they do with the data in hand and what analytics they do with it. Data immature companies noted to miss some crucial roles say, for example, they have Data Scientists and not Data Engineers, with which the data ecosystems may not productize properly. Sometimes, data scientists and analytics experts would be scattered across an organization without any proper structure, and the hiring of such professionals are done by individual decision-makers, but not well aligned with the company-wide strategies.
In fact, the terminology related to data management is also changing. By the year 2016, the term “Big Data” was the vogue, and before that, it was “Advanced Analytics.” The modern-day companies which function in the digitally native industries are applying with advanced AI and data analytics methods in their business, but many older companies are not doing it yet. The need for digitalization and its resulting changes for advanced data and Ai methods have taken over many business organizations by surprise and changed their business models.
Now, the leading industries experiencing the most competition from the digital-native sectors like retail, media, information technology, etc. have to transform their operating models to adapt to better data utilization. In contrast to it, there are many manufacturers, and production-oriented companies too are at the frontline of the data transformation revolution.
As a result of this increased data awareness, many of the established organizations now conduct sophisticated AI and data programs with huge expectations around their business turn around and also start to attract more talent. However, after a couple of years into such innovative programs, many of them start to show the early signs of operational fatigue with expectations being unmet and the business leaders becoming unhappy by being unable to see any progress.
Many pilot projects in some business areas have indeed launched innovative data-enabled products and services as what RemoteDBA.com offers, but when it comes to data enabling transformation at large-scale business ventures, it is yet to come. With this, we can consider AI and data as niche activities still, not to set premises for business.
When it comes to data best practices, there are no shortcuts. The Information Technology giants like Google, Amazon, Facebook, and Apple all now use totally different strategies in their business to gain proper traction on the market and make a global influence. However, the common secret behind their success stories is inarguably their data propositions and the foresight they gain to position themselves rightly. They used to work from inside out and then keep a keen focus on building capability alongside developing and deploying top-end technologies internally.
For the established companies which are into non-digital industries, the track for data management and AI seems to be a bit bumpy. Older existing companies have established unique ways of working with their legacy infrastructure and digitally immature workers. Transforming such an operational model calls for very stubborn determination from the leadership. It means they have to bring data and business intelligence to the core of their business approach and decision making.
From building business strategies to operational models and aligning the data-centric decision making, their actions manifest a focus on the database and artificial intelligence capabilities. What will take them forward successfully is the agenda of the forward-thinking HR managers who can understand the importance of digital talents?
The business leaders of the future must be involved more into different aspects of data and Ai strategy execution and business capabilities involving the supporting initiatives. Considering this fact, we could also see that committed leadership may stay as the common denominator of success in digital transformation. It is the business leaders who first learn the need for becoming data-driven, but most of the time has limited skills and knowledge in technologies. So, now, many of the universities and consulting firms are offering data and artificial intelligence training for business leaders.
While practicing data analytics and artificial intelligence, beginner business leaders tend to make mistakes by focusing more on statistics and coding with their desire to understand AI. Even though coding is an essential skill for data engineers, business leaders may focus more on creating an ideal organizational environment for effective data management. This means the business leaders should focus more on setting up the business goals, identifying the right professionals, educate the employees, making ideal investments, and ultimately implementing the most effective operational models for data and artificial intelligence. This could be done at best by defining the goals clearly and then closely following those up.
The ideal premise for proper data and artificial intelligence strategy is to know the right business goals. Identify what your challenges and battles to win are? Consider your strong and weakest points and where you have to succeed? Effective use of data and AI would help the business leaders to make more informed and insightful decisions, automate the business processes and enable the fastest delivery of their goods and services.
All data and AI priorities should be derived from business priorities. As data and AI make contributions in certain unique areas of business by keeping the company’s goals and objectives in mind, business leaders need to consider each business case as a unique project and plan the AI and data strategies to optimize each.