How is Bad Data Crippling Your Data Analytics?

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data analytics

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 more widespread harm can be caused by bad data in Data Analytics, where anything from the wrong medical diagnosis to incorrect interpretation of stock history can cause service providers to close shops or face lawsuits.

With the wide proliferation of big data, the Internet of Things (IoT), and real-time analytics, the chances of acquiring huge volumes data at high speed is assured, but the current Data Governance processes of many organizations are still not sophisticated enough to trap the inaccuracies in such high-speed and high-volume data. The Result? Bad diagnosis, bad predictions, and missed opportunities across all industry sectors.

The article What Does Data-Driven Culture Look Like? demonstrates how high volumes of data from many different sources have continued to affect the business ecosystem. In a data-driven business environment, the continuous evolution on newer data sources and more complex data types has necessitated the implementation of sound Data Governance mechanisms without which much of the data will remain as noise without any substance.

Business owners and operators have access to huge amounts of data they do not trust, especially those emanating from new data sources.

The Rising Data-Driven Business Ecosystem

The article How Bad Data Can Break Your Business shares the following important statistics about data-enabled decision making in businesses:

  • 40 percent business executives make major decisions at least once within 30 days and the data they rely on to make these decisions are rapidly rising at an upward rate of 40 percent per year.
  • A Gartner study states that about 40 percent of enterprise data is either inaccurate, incomplete, or unavailable, which results in businesses failing to achieve their data-driven goals.

This author of this article makes an interesting observation; the speed of incoming data that looks intimidating now will multiply many fold when the Internet of Things reaches full maturity. Thus, the possibility of disconnected data silos, human errors, lack of system integration, and failure of data migration are real threats to Data Management of the future. The businesses who quickly recognize these problems and plan for centralized Data Governance are surely ahead of their competition. In the near future, Data Quality will supersede technology footprints in ensuring business success.

The Cost of Bad Data

Yes, bad data can cause a huge loss to companies in terms of lost opportunities, reduced revenues, and customer attrition. In the world of big data, these threats are more prominent, which is confirmed by Gartner. According to this reliable market watcher, lack of Data Quality control costs average businesses $14 million dollars a year.

  1. Cleaning of incoming data
  2. Standardization of data
  3. Monitoring of data
  4. Centralized control of data (Data Governance)

What Is Poor Data Quality Costing You? repeatedly states that in an era of the engaged customer, the quality of customer experience is what makes or breaks a business.  As most businesses have gone digital or maintain a digital presence, a substantial portion of the customer engagement with the vendor happens online. The 360-degrees view of the customer is now a crucial competitive edge for businesses.

So how do vendors gain this 360-degrees view of the customer? Simple – through customer data acquired through a variety of digital touchpoints. As businesses increasingly depend on customer data for improving their customer service, the quality and value of the incoming data will play a major role in customer analytics.

At the end of the above report, the reader can find a useful questionnaire to evaluate and monitor Data Quality. A Kissmetric post indicates that business can not only save dollars from a solid Data Governance framework, but can also earn a solid business reputation for being reliable.

The Imminent Challenges Facing Data Analytics

  • Poor Quality Data

Take the case of procurement industry. The article titled Data Quality and Governance Are Biggest Challenges for Procurement Teams aptly describes how the lack of Data Governance has stymied the performance levels in the procurement industry. The article talks about a CPO Survey, which indicates that bad data is the primary reason for poor quality analytics in this sector.

The primary reasons behind bad investment decisions in  Bad Data: A 21st. Century Epidemic, you will notice that inaccurate, incomplete, or unavailable data can lead to poor risk assessment, incorrect financial data, or erroneous loan applications. Such bad decisions can not only cause customer fallouts but also poor business reputation.

  • Disconnect between Analytics Sub-Systems

In the investment industry, business operators or service providers using legacy backend systems often have to wrestle with the lack of continuity between the backend, the middleware, and the frontend systems. In this sector, the critical need of the hour is to roll out data platforms that provide integrated back, middle, and front ends for maximizing operational efficiency and agility.

The single view management of “risks, security forecasting, reconciliations, valuations, and accruals” can greatly enhance the effectiveness of investment brokers. Integrated Data Management systems can help business operators achieve high ROI from their technological investments.

The Importance of Data Quality in Analytics

Business analytics is one area where the need for clean data cannot be overemphasized. Many current data service providers have now transitioned into cost-friendly packages offering bundled data collection-cleansing-preparation-analytics services.

Many of these services are cloud based and offer economical, data solutions that medium- or small-sized businesses can use. Due to the rapid commercialization of managed data services, more businesses of all sizes are now adopting clean data strategies as part of their core business activities.

The article Five Ways to Maintain Data Quality in Your Analytics  states AT Internet conducted a recent study on the role of Data Quality in digital analytics. The article provides some tips on how to monitor the quality of data on sites that frequently update their content.

The Importance of Data Reliability in Analytics

Numerous business activities today are largely dependent on the data pipelines that collectively provide business timely competitive intelligence or operational wisdom for survival. This is more so because big data has facilitated the use of multi-channel, multi-variety data from disparate customer touchpoints. IBM Integrating Governing Big Data discusses how metadata, data integration capabilities, and Data Governance all jointly contribute to the quality of data that is used for day-to-day business analytics

The article How to Avoid Being Deceived by Data makes a highly convincing case for the reliability of data. According to this article, data-driven activities like A/B testing have to solely rely on data samples to evaluate results. Here it is easy to understand why a bad data sample, which is “representative” rather than “actual,” can adversely affect the results.

In Almost All UK Law Firms Are Vulnerable to Email Fraud Study Shows, the author claims that a fairly recent study demonstrates that almost all UK-based law firms use email systems that are largely prone to data piracy or fraudulent use by fake accounts. The email security firm Mimecast reports that information piracy has increased by almost 40 percent in the last few years.

This email security firm further states that as any email system is the first customer touchpoint for the law firm, any fraudulent user can access the mail domain to distribute fake messages to external clients.

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