The Importance of Digital Transformation in Predictive Analytics

Using data to gain actionable insights into what customers want is fast becoming less of a competitive advantage and more of a standard business optimization strategy. The digitization of business and the nature of a 24/7 connected society means that there is more data available today to predict the future than ever before.

So how do you turn your data into actionable insights that provide you with real business value? The answer lies in transforming the data and thinking carefully about the data formats that you should leverage first to gain business insights and forecast the future.

The failure rate of digital transformation projects hovers around the 70% mark, according to some industry analysts, so it is imperative that companies not be distracted by the sheer volume of data available. Of course, the quality of the data itself must be considered, but there is a bigger problem in figuring out whether to focus exclusively on structured data or also include unstructured data in the analysis.

Before you start your next data-driven initiative, consider the following:

1. Decide which data formats to take advantage of first. It can be tempting to go all out from day one with both structured and unstructured data, the wiser path is to start with small data mining, transformation and loading (ETL) pilot projects, and once ROI has been established in terms of the insights provided, upgrade Accordingly and harness the power of unstructured data with Extract, Load, Transform (ELT) processes.

2. Decide in advance who will be responsible Data Transformation and Governance.

While some smaller companies may be able to hand the job over to a chief data officer, it often takes a dedicated team to build data-driven initiatives and grow workforce skills as required.

3. Decide how you will rate data quality Both for input and output and for communicating the value of spending time and money on digital transformation to stakeholders. It’s important for everyone in your company to understand why analytics software needs adjustments to be truly effective and the role digital transformation plays in reducing the chances of getting false insights. Decision makers who not only understand the value of the data they currently own – but also understand the importance of using that data to discover actionable insights, will be able to predict the future and succeed in a competitive market.

In recent years, data has become a commodity to be coveted, with people, places, and things generating information at an astonishing rate – for example, the World Economic Forum predicted in 2019 that 463 exabytes of data would be generated per day by 2025. If the predictive analytics journey begins with a search for Hanging fruits, you can be sure that the vast amounts of available data you collect can be used, maintained, and accessed in a way whose value is undeniable.

The challenges of using unstructured data for predictive analytics are not limited to large organizations or those entities that are still in the early stages of their digital transformation. Turning data into an analytic state has proven to be a stumbling block for companies of all sizes.

By converting available data from one format to another, you can take advantage of not only data integration and data management requirements, but also facilitate data exchange and data warehousing initiatives. Your data may be gold, but if it can’t be easily converted into a format that allows actionable insights, you can also call it digital dust.

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