When organizations want to be data-powered, they have to give preference to data transformation over digital transformation.
Through an age that is digitally empowering, corporations worldwide are keen to harness the potential of AI, big data, and machine learning. The rate of innovation is high. Companies invested millions of dollars in data lakes, moved to the cloud, and recruited data scientists and data managers to carry out their digital transformation plans.
They crash, however. Amazingly. Most studies and surveys indicate that there are specific factors in more than 85 percent of Big Data ventures.
Recently enough was written about how corporate cultures and unchecked ambitions lead to failures in big data projects. This article focuses on the often-ignored poor data quality and is one of the leading causes of the collapse of the digital transformation.
The uncertainty between digital transformation and data transformation.
The method of converting raw data in a functional format is frequently incorrectly juxtaposed with the digital transition. Companies assume that they are transforming their data because they implement data lakes, data centres or new ERPs (all of these are intrinsic to digital transformation).
This is a risky assumption. This takes the emphasis from the actual issue and gives companies an illusion of protection. New structures should solve challenges and help to accomplish, but do not achieve, transition goals.
The latest ERP introduced by the organization six months ago will not improve operating procedures because data problems were not dealt with in the legacy program. The new CRM in the field of your marketing team does not return the expected ROI because the team has no data governance or data quality framework in place.
An organization can prevent expensive errors by understanding the distinction between physical and data transition. To be data-powered, companies must first grasp their data, address inconsistencies, and convert data. Digitalization is the end of the cycle – the transformation of data is the beginning!
Common data issues overlooked by business leaders
With Fortune 500 customers, we worked on digital transformation as a priority only to find out that they were not ready to do so. In general, some of the common problems are:
- In disparate sources, data siloed away. When the corporation expands, more data is stored in a multitude of databases, which helps the organization to interpret their knowledge poorly and incorrectly.
- A method of gathering human-dependent data. There is also a high risk of incorrect data being manually entered by men. The leading cause of problems with data quality will always be a human-dependent data collection process. A mistake of typing, a contextual knowledge of a name or place, a missed number, etc. are all small cases that degrade data quality over time.
- Duplicated data for eons in the dumps: for many purposes, a business may collect the same customer data. Year by year, hundreds of different ways are recorded in several data sources for the same consumer data. An insurance company found it difficult to disclose annually because of redundant data gathered over the months. A distributor had to postpone six months' expansion plans as their data didn't make the right picture.
- Data that does not provide a single source of truth: a bank has struggled to build customized consumer experience since each of its services (credit, mortgage, small business loans, insurance, etc.) has its data sources. As different bank services have been used, the customer information would repeatedly be replicated. The bank could not grasp the journey of its customers and could not deliver personalized experiences without a consolidated view of its customers.
- Data not prepared for business intelligence: Data preparation or discussion is a technological ETL method (Extracting, Transforming, Loading), but with real effects on the environment. Data not prepared; for business intelligence, it is impossible to use that which is not cleaned or optimized. If a company wishes to obtain competition or critical insights, it will not be able to do so with incomplete, outdated, obsolete, and duplicated data. Bad data, incorrect data or dirty data are the consequences of any of these triggers. Over time, wrong data is becoming an emergency, a security breach, a tragedy that can ruin your business.
What is wrong with the data and why do business leaders ignore it?
An old but relevant study by Gartner found that low data quality is a primary explanation for 40% of all company projects struggling to achieve their target benefits at all times. Although this report is ten years old, the time remains for this research. At present, when companies use over 400 applications on average, data are being transmitted continuously, most of which are raw, dirty, and unusable.
The maintenance of this data and the assurance that the company has data to trust is a routine activity to be done by everybody in the organisation. The response to the second part of this question lies here.
Business administrators neglect issues of data consistency, as data is now an IT liability for eons.
- Incorrect data obtained by members of sales, marketing or customer support? IT is in charge of cleaning it up.
- Do C-level managers need analytical data? The IT team must clean up the data and submit reports.
- Costly returning mails due to faulty contact addresses? IT needs to ensure the validation of addresses.
Nearly every data problem in the IT department is siloed. Business leaders, including C-level staff, are either unaware of or are not interested in solving issues of data quality. Compared to other big strategies for change, data quality challenges seem so insignificant that policymakers ignore them entirely. Of course, the IT department will take the blame until the transformation plan is stalled or fails.
The fundamental explanation of why experts discuss 'the culture of businesses' as one of the leading causes of transition failures is this disconnector between IT and C-level suites. The burden must be shared among business teams for an enterprise to be data-driven.
Why would data transformation be given priority over digital transformation?
Because new technologies rely on precise data, you need data you are willing to trust, whether you build the next generation robots or banking big data to understand your audience.
Thus, data transformation is the means to achieve the digital transformation objective. As from our experience, companies that actively process their data and have implemented data management have been able to increase their ROI, optimize their operating processes, make valuable use of their staff and ultimately become a digitally enabled company.
The conclusion is simple – you need data that you can trust to be data-powered. You must enforce a data quality system to acquire the information you can trust.