Data Transformation and Digital Transformation in 2021
Digital Transformation

Why Data Transformation Is Level 0 For Any Digital Transformation

We are living in an era where the data created daily outnumbers the number of living beings on the planet regularly! 

By 2025, IDC predicts that connected IoT devices globally alone will produce close to 79.4 Zeta Bytes of data. 

Despite being surrounded by a vast lake of data all around, it is very disappointing to know that a lot of this data is simply wasted. Every interaction that people have in today’s virtually connected world carries a treasure trove of data within its realm. While tech giants like Google and Facebook have been constantly making efforts to monetize as much data in their hands as possible, other organizations have simply not been so excited at the prospects. 

A Forrester survey pointed out that most firms do not make even 50% of their decisions based on data insights. They mostly rely on their experience or varied personal opinions, or even the gut feelings of leaders. This is not a good sign at so many levels. As more startups and tech companies rise to prominence solely by capturing and extracting valuable insights from consumer data, there is an imminent threat to the very existence of several established business houses if they do not find ways to uncover value from their huge data banks. 

Read: Why Digital Transformation is the Only Saving Grace for Small Businesses to Survive in the Post COVID Era

Uncovering value from data is the final steppingstone to proceed with digital transformation. Before the actual insights from data are used to drive decisions, there is a need to have data in a format as desired by today’s powerful digital platforms and analytical processing systems. 

Data, as we know, exists in different formats across various enterprise systems and consumer digital avenues. For decision-making, it is important to transform raw data into formats that support further analytical processing, which is the foundation of insights generation. 

Data transformation is a critical component of any digital strategy. It can be defined as the process of transforming data from different digital systems and transactional processes into formats required by advanced analytical computing tools and platforms for generating insights. Data transformation is to be considered as Level zero for any organization that has an ambition of digital transformation.

Let us examine the top 3 reasons why data transformation is critical for an organization to succeed in its digital transformation journey:

Efficient Organization

With data transformation, raw data gets ordered and organized into a format that is easier for understanding by both humans and machines. It allows for faster sequential or random processing as it becomes easier for connecting inference points for analytical processing.

Faster Collaboration

In almost all organizations, there will be a number of departments working as independent units in their operations but have to work collaboratively to deliver stellar experiences to customers as well as their employees. In such settings, there are several avenues where data from one department has to be consumed or connected with the data stream of another or a couple of other departments to arrive at a final decision. Without data transformation, each of these units will generate data and the whole decision-making process will not be reliant as it operates on data residing in siloes. In fact, data will not even move to the processing stage in most cases as different departments will be having a hard time processing the exact information from data supplied by another department. To solve these, it is important to have a data transformation strategy in place that will manage policies that govern the interoperability of data between departments and ensure smooth data flow. 

Improve Quality of Results

When the right data with the desired parameters are fed into analytical systems there will be a marked improvement in the quality of results derived. Digital data generated by different enterprise systems managed by different vendors could result in a complex mesh of scattered data that is fed into analytical systems. Without data transformation in place, there are chances of erroneous data or unverified instances of a data item making it into the analytical stage. This could corrupt the entire final result. When sensitive processes like financial transactions are managed with incorrect or erroneous data, there could be serious implications and trust issues that will arise and could impact the reputation of the business itself. 

As we progress into a world that is increasingly being hyper-connected, businesses that do not innovate in their business models are certain to bite the dust. To continuously re-invent themselves, businesses have to look at making the most of their biggest asset i.e., data residing in their digital landscape. Failing to create a strategic path for data to be transformed into the right format before being put into powerful systems that utilize analytics and data science to generate insights, will be suicidal. Without the right data, companies have no option but to watch from the sidelines as more data frugal organizations will disrupt their businesses in a matter of time. The best way to avoid this shame is to have a strategic direction in enabling a more seamless data transformation practice in your business. This will propel your organization’s journey to digital transformation at a faster pace.

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