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Data is, in multiple ways, the platform for a digital transformation strategy for organizations. Regardless of company size and industry, many businesses fail today when trying to implement a digital transformation data strategy. In fact, when organizations try to leverage the right uses of their data, only a small percentage is typically strategically analyzed. According to a recent study by Talend Group, only 45 percent of organizations’ structured data is actively used for business intelligence. A good data strategy involves the analysis of internal and external data, so data can be harnessed as a valuable strategic asset in all facets of the business.
Data quality over quantity
Knowing how much data you have in your organization is less important than knowing what it takes to successfully leverage this asset. Amassing huge amounts of data is a worthless endeavor if you do not know what to do with it. It is critical for businesses to understand the indicators that data can provide in order to grow the business.
Data continues to grow at an increasingly rapid pace over time. IDC (International Data Corporation) predicts that the collective sum of the world’s data will grow from 33 zettabytes this year to a 175 ZB by 2025, for a compounded annual growth rate of 61 percent. A zettabyte is a trillion gigabytes. Now multiply that 175 times. As a result of new cloud services, more data – both structured and unstructured – is stored and accessible for analyzing in massive data sets. Organizations now have more opportunities than ever before to generate powerful, growth-generating business insights, but they must first focus on generating quality, purpose-driven data.
Data strategy drives business value from technology investments
A well-conceived and implemented data strategy profits companies in many ways. Data analysis enables customer predictions for improving user actions. With many devices being interconnected through IoT (Internet of Things) and sharing information with one another, the right strategy also helps companies to achieve better predictions in order to make recommendations or to suggest actions. For instance, companies are increasingly leveraging the IoT by having interconnected devices in production and distribution facilities. These IoT-enabled devices, reporting information through an enterprise resource planning (ERP) system, provide real-time information on inventory demand for decision making.
Companies that adopt new emerging technologies (e.g., RPA, artificial intelligence, machine learning, blockchain, big data, cloud computing, IoT, and 3D Printing) use data for improving decision making and for obtaining operational efficiencies such as cost reductions, better visibility into business processes, real-time information, and advanced analytics.
An artificial intelligence project needs specific data for applying algorithms that will create the best outcomes. Through machine reinforced learning techniques, the more data the project is given, the more the systems self-learn through algorithms gradually become more accurate. Computers today can learn autonomously from their own data through machine learning technologies, and the more data they receive, the more they improve their algorithms themselves for better decision making without human intervention.
In Part 2 of this two-part series, I will explain the impact of data on automation, which is fundamentally changing how businesses operate. I will also provide suggestions for data security, and for developing your data strategy action plan for better business decision making.
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