What if 80% of Your Data Sucks — And You Don’t Know It!
Data is credibility – your credibility. It really is that simple. Look around, data has changed the world’s affairs into something much greater than an individual mind can handle. Data is the essential driver of the innovations we see around us, and also how we differentiate in our business. Big Data just refers to data that’s is high volume, has wide variety or is supplied in high velocity. Big Data is still just data that needs to be effectively data managed to make sense out of it.
To illustrate the importance of Data Management and Data Quality, think of what could happen if majority of your data has problems – What if 80% of Your Data Sucks and You Don’t Know It. It’s probably true, so you should be thinking about it. Around the 80% fail rate is what Tom Redman finds when people apply his gauge to determine whether what they have is a good or bad data.
Read on… or get serious and register for Tom Redman’s Data Provocateur Boot Camp.
What is a Bad Data? It is information with errors, that is misleading, formatted unclearly, duplicated, and the likes. Unfortunately, most businesses retain this kind of data for the very reason they should clean it up – they know it’s value. If you do not know the quality of your data, you’re ignoring the importance of good data to your business and blind to how to adapt your data quality management process to avoid the consequences of poor data. Here’s the real story on having poor data quality:
1. Distorted Data leads to targeting the wrong market or customer base
All businesses need a clear vision on whom they are targeting to gain brand awareness and sales. Distorted data supports the wrong thinking about your target market. Having insights that are off the mark leads to missed opportunities, wasted marketing and higher costs.
A business that does not know how their real target market behaves and is evolving will never be in front – Getting in Front on Data means also getting in front of your target market.
2. Negative Financial Impacts
Incorrect information brought about by bad data quality increases operating costs, ruins forecasts, decreases revenues, reduces growth, delays in cash flow, and can even increase penalties and fines.
3. Decrease in Productivity and Efficiency
Poor data quality destroys trust in a business’ overall operational and management reporting which then leads to unreasonable workload on employees; decreasing throughput and producing low quality finished product or service. Management by gut feel replaces rational decisions as people in your business don’t trust the numbers – even though it could be their own data!
4. Increase Investment Risks Equals To Low Returns
If a business uses poor quality data as a basis for their investments, they also increase their risk, maybe unknowingly. Consequences are inaccurate projections, zero or negative return on investment, and maybe worst of all, missing opportunities.
The negative consequences of bad data can be prevented by knowing how to examine and process data properly – whether you’re a multinational or start-up, it is up to you to know your data.
It is worth investing in your business to clean up and focus on your data. After all data is changing the world. It might be helping to cure a disease, boost a company’s revenue, make a building more efficient or responsible for those targeted ads you keep seeing.
Start learning how to improve your data by learning from the “Data Doc” himself, Tom Redman.
Tom has personally helped hundreds of leaders and companies better understand data and data quality and advance their data programs. In addition to those noted above, he’s written dozens of articles for publications such Harvard Business Review, The Wall Street Journal and MIT Sloan Management Review. Catch him live during his Australian Tour this October 2017. Click here for details.