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By Eric Thain, General Manager, HK Express
There is no doubt on the importance of data in today’s businesses. The C-Suite recognizes the need to focus on data and in fact, data and analytics ranks the highest of the top five investment priorities for CEOs today (according to the Forbes Insights and KPMG Global CEO Outlook). However, the trust & reliance on data-driven decisions remain low. Two-thirds of CEOs say decision-making is only somewhat or rarely data-driven.
For the longest time, before the gigantic tide of data swept upon businesses, the lifeblood of any seasoned, successful business leader is experience and intuition. People are normally comfortable with the status quo in decision making and most executives don’t process the world in a data-driven & deeply analytical way. Now that businesses are facing this giant algorithmic leap, the executive team will be finding it harder by the day to keep up with the enormous variables in the business cycle without relying on trusting data. Machines simply haven’t been put to work at what they do best.
So why is this? A company may possess a huge quantity of legacy data that has existed in silos for the longest of time. Legacy IT operations struggle daily to cope with and derive value from huge amounts of data generated in dynamic infrastructures and applications. The inaccuracy and duplication of data from these silos make their jobs even harder. This not withstanding the scale of data that we are collecting from the consumer from new sources of data every day. Many are from unstructured sources like social media and messaging that is hard to integrate into existing systems. For example, most of the Internet data is completely unimportant & irrelevant to a specific business. It is key to reduce the amount of noise to identify interesting patterns, conversations that answer specific questions.
Regardless of the hesitance on the data front, the fast pace of technological change and the competitive landscape mean that businesses must quickly scale up capacity and integrate data to leverage the world of possibilities that it can offer.
Big data continues to change how we do business, inside & out. As such, CEOs shouldn’t be held back from trusting data. To become a data-driven organization, leadership needs to set the tone. Business leaders must do more to close the gap and use advanced analytics insights in order to make the evidence-based case for change and fully capitalize on the strategic benefits of data-driven business initiatives.
What’s new today is the capabilities for advanced analytics, including predictive modeling. This will enable us to link data sets that have not been linked before to influence future outcomes. Companies should be thinking about how to frame the problem, how to take advantage of the available data that’s available, and the different approaches to use the data. Leaders should manage advanced analytics groups within a well-aligned framework across the enterprise, departments, and lines of businesses. There are many efficient software frameworks out there that cater well to do this.
Prescriptive analytics improves its prediction accuracy and provides better decision suggestions by continually ingesting and processing new data
Use-case-specific solutions offer business user-friendly interfaces that conceal complicated models and simplify user experience which works well for the companies who are just embarking on this data journey.
When business requirements become more complex, the limitations of use-case-specific solutions become apparent and must then be further developed to be highly organized and context specific. This is often an evolution that occurs as advanced analytics capabilities mature.
Taking this a step further, artificial intelligence (or more precisely Machine Learning) has a big role to play.
Machine Learning is progressing very quickly in the big data world and many of the AI companies are offering solutions that are making big differences to brands’ bottom lines. It can help businesses unlock the potential of their data by processing internal & external, multipoint & unstructured data across the whole business such as sales, operations, marketing, social media, e-commerce etc. By doing this, it connects the dots & delivers actionable consumer insights to a brand that drives bottom-line ROI.
The real value of machine learning comes from its ability to create predictive models which can guide an organization’s future actions and discover never seen before patterns. Though only gaining popularity in recent years, AI and machine learning is not a new technology per se and has been utilized by many mature data reliant companies. However, many companies expect to experience greater satisfaction with data-related technologies as predictive analytics and machine learning mature.
Businesses are also becoming more complex and decisions must be made in near real-time, which require a data-driven approach beyond descriptive and predictive analytics. In comes Prescriptive Analytics.
Prescriptive Analytics can be seen as the future of Big Data. Prescriptive Analytics go beyond predicting outcomes by actually suggesting and automating the action CEOs can take to achieve the desired result, to improve operating accuracy, efficiency and drive organizational strategic alignment.
Prescriptive analytics is used in scenarios where there are too many variables, options, constraints and data sets. This scenario is prevalent in industries, such as airline industry, that have utilized advanced analytics in various part of the operational & business cycle. Airlines adjust prices and promotions automatically based on supply and demand for their online customers without any human intervention, leveraging prescriptive analytics from complex business rules and data models. In addition to transport optimization, other industries that leverage this with effective business outcomes can be seen in the healthcare industry where there are massive amounts of different patient & historical data sets.
Prescriptive analytics helps companies take advantage of future opportunities or mitigate future risks as predictions are continuously updated with new data that comes in. Prescriptive analytics improves its prediction accuracy and provides better decision suggestions by continually ingesting and processing new data. This technology provides leaders with the ability to use probability distributions to reduce risk in decision-making.
It is obvious where the future of business lies and the indispensable role data plays in it. However, as current adoption shows, there is still a long way away before it becomes common language. Those who are quick to adapt will have a distinct advantage over their competitors and derive the actionable value to cater better to changing consumer landscape.
As more data is unified and created across the enterprise, leadership has the opportunity to ask better questions and leverage an asset that their competitors do not possess – insights about their operations and customers. Ensuring advanced analytics initiatives are closely aligned with the overall business strategy and how the organization operates can create competitive differentiation.
Companies need to jump on the data analytic bandwagon before it leaves the station for good. It’s time to walk the walk.