Understanding the Reason Behind Wrong Enterprise Data Planning

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Data transmission channel. Motion of digital data flow. Transferring of big data.

How bad is wrong data handling and management by enterprises?

Companies today generate more data than ever. Data, can give them an unprecedented opportunity to gain insights into consumer preferences, behaviour, product lifecycle, supply chains and targeted marketing. Despite this, few enterprises rely on data to capitalize on the growth opportunities. There is rarely a single reason why organizations fail to reap the benefits of their data platforms.

Some striking evidence of the impact of bad data can be found in fake email IDs, impersonations on social media, or misuse of stolen financial or personal information. The wide proliferation of Big Data, the Real-Time Analytics, and the challenges of acquiring huge volumes data at high speed rises questions on Data Governance processes in many organizations Add to it bad diagnosis, predictions, and missed opportunities across all industry sectors.

Demystifying Data Silos

An abundance of data can easily become the very roadblock to decision-making that it is meant to remove – overcomplication leading to inaction from the people responsible for growth. While there are a number of reasons why data can create silos, here are a few that are most common:

Not Identifying Data Objectives

Companies race to capture data and analyse it without a clear end goal. A clear data strategy tied to business objectives is key. Data strategy needs to focus on measuring the progress of a project, solving a problem or gaining insights.

Unstructured Data Assimilation

A problem with multiple sources of data is that it invariably provides varying information, making it hard to know which source to rely on. One solution to this would be to remove the clutter, by eliminating data streams that have the least effect on decision-making and focus on the ones that help generate results.

Fixing Data Decisions

Multiple sources of data capture and analysis are other factors that influence data decisions of an enterprise. Expecting the same types and volumes of data to inform every business decision can result in data complexities. Enterprises can take the requisite decision based on the right amount of data it deserves based on its importance.

Perfectionist Approach

While taking data driven decisions, enterprises must not focus too much on perfection unless the decision impacts the future of the business. Making decisions based on data analysis should be considered a step in the right direction of a larger objective or goal. Instead enterprises must focus on taking smaller decisions that make incremental improvements. This gives a much-needed breathing space in case some of the decisions don’t yield the desired results as expected.

Relying on Duplicated Data Resources

Enterprises have an unparalleled access to multiple sources of the same data; this may lead to data duplicity expanding the cost of data analysis. This leads to increased cost in hosting, analytics packages and labour because each department will need separate budgets to manage their own data management practices.

Cost of Bad Data Management

Bad data is expensive to enterprises, and has the potential to cause a huge loss in terms of opportunities, reduced revenues, and customer attrition. In the world of Big Data, these threats are more prominent, says Gartner. The lack of Data Quality control costs an average of US$14 million dollars a year to businesses worldwide. This loss is segregated to these parameters-

1. Cleaning Incoming Data

2. Data Standardization

3. Data Monitoring

4. Data Governance

In a crux, the quality of customer experience extracted from clean data sources is what makes or breaks a business.  In the digital data age, the 360-degree view of the customer is now a crucial competitive edge for businesses worldwide.

To capitalise on opportunities, enterprises need to leverage customer data acquired through a variety of digital touchpoints. As businesses increasingly depend on big data for their strategic objectives, the quality and value of the incoming data will play a major role in winning the ultimate data dominance race.

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