Those who apply statistical techniques to derive insights would be the future success stories
The challenge business leaders face in determining the level of investment to be made in analytics is that they are often unable to ask the right questions in a fast-evolving space. As a famous quote goes, “If you don’t know where you are going, any road will get you there”. In a digital world, the winners would be those who know what questions will give them sustainable competitive advantage in the future and shape the solutions they demand and get. Getting analytics right is the key to successful digital transformation of business.
The most often asked question from the leadership is “what did we do right and wrong last month?” Given the investments in computing, the assumption is that these questions do get the right answers, but to succeed in competitive environments, future questions that will have to be asked are “why did whatever happen, positive or negative, happen and what can we do to ensure the future will be what we want it to be by mitigating the risk of negative happenings and create more positives? Can we change some factors to guarantee more positive outcomes and is the data we are seeing, telling us something that can change the way we approach the future?”
Systech, a California-based business intelligence and analytics consulting company, provides a way for CEOs and corporate strategists to look at analytics when it describes the path to success as the transformation of raw data to useful information that provides valuable actionable insights into business performance. Framed like this, it becomes obvious that the corporate success stories of the future would be those who move from the descriptive analysis comfort zone to applying statistical techniques and algorithms to derive the insights that make predictive and prescriptive analytics a reality for the organization. This methodical and iterative exploration of high-quality data using skilled analysts and, when needed, data scientists will be the hallmark of companies, which practice decision-making driven by data.
A few examples of industry leaders will throw light on the approach being taken today. An manufacturing started a few years ago with the implementation of a centralized data management infrastructure and business intelligence architecture that enabled a 360-degree view of business operations from customer demand to shop-floor operations and logistics, including supply and demand chains. After some initial forays into building a leveraged technology stack and data stores for data exploration and mining, the company has now deployed prescriptive analytics models that proactively drive growth strategies and measure results as they happen. The data cleaning and distribution has been automated and done in real time to improve organizational responsiveness and accelerated analytics.
Another case of analytics transforming both efficiency and revenues is a leading auto insurance agency, which has transformed its entire data management ecosystem and developed the ability to trap data from sources far exceeding the traditional structured ones. In the fast changing world of digital immigrants and natives, the ability to handle structured as well as semi-structured data and provide quick insights to agents and employees of the firm could not be handled by traditional methods; the solution necessitated the design and deployment of a big data architecture that would provide low-cost data storage, use tools to parse and expose every variable within low volumes of semi-structured data and substantially reduce the time in which pre-processed data could reach analysts at near real-time speeds. The final test of success has been the effectiveness of presenting the data in a form that could be easily analyzed and visualized for decision support purposes.
With today’s increasing sophistication of business analytics tools — for big data management, data visualization, statistical analysis, business intelligence reporting and analytics through served reports or self-service — data scientists are being called on to ask open-ended questions of data and provide insights far beyond even the reach of questions that have traditionally been asked of from data. There is also a tendency among new users to quickly move to self-service tools like Tableau and Qlik, which often give the CXO suite the perception that clever presentation of data is the purpose of analytics. However, firms that handle large amounts of data not only generated by information systems but also through sensor beacons, shop-floor machinery and Internet of Things devices should not settle too easily for convenience. Rather, they should have the right teams to explore machine learning and deep learning, integrate sentiment analysis from social listening and offer points of view to decision-makers that can provide true competitive advantage.
For the CEOs and strategy chiefs watching their digital transformation agenda unfold with optimism as well as some trepidation, there is something to think about. Digital transformation is too important an agenda to be just left to technologists. Business process optimization and re-engineering, building a culture of digital understanding and practice across the length and breadth of the organization and its business partners and ensuring that analytics moves from a post-mortem description of the past to a truly predictive and prescriptive driver of the business — these are three pillars that must be erected in addition to the technology stack to ensure that digital business flourishes to take the organization to success in the future.
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