For any company, data is considered to be the most important asset; not working on the maintenance of data leads to loss not just in term of money but also in terms of human resource, internal operation, efficiency and many more. There are foul consequences of Poor Data Quality amongst them top 50 are shared for your perusal.
1. A delay in need is a delay indeed!
A certain decision in the organization is supposed to be taken on timely fail in which it the organization be thrown back from the pie of competition and customer or co-partners will not appreciate out of date enterprise to deal with.
2. Defamation is a Demon
When the customer will get the essence of companies work, they will give up the value that the business gained in their eyes. When a company keeps on sending emails or text to a customer whose data were not updated they not just lose them but when a customer comes to know about this fact; they defame the company by way of words.
3. Absolutely Obsolete
The strategies are supposed to be crafted drafted and executed in time to have a competitive edge over the market but in case an organization fails to do it on time due to Poor Data Quality it will soon become outdated.
4. Revenue killer
The end process of the data gives a final outcome and on the basis of which the company implements its strategies; the lame data gives the worst possible solution that lets the revenue commit suicide.
5. Opportunities will not cross the way
The entire root of the tree is based on the seed that is planted way back before the tree starts sprouting; same is the way with opportunity; it shall only sprout when the seed of correct data is at its base. If you don’t plant seeds there will be not birds on its branch and if you don’t have right data you will not strike a great deal business deal.
6. Returns on Investment turns into Return on Risk
Any businessman starts the venture with an idea to earn profit and not risk. Outdated data will not get business the returns but in-turn will attract risk that will lead to the question of the existence of enterprise in the long term.
7. Encompass prediction in the wrong direction
The basic use of the data for any department of the organization is to get the trend in their respective field but when an organization deals with dumb data it encompasses the prediction in a vague way.
8. Cost of job increases
The data gives you a way by which you understand the appropriate use of some miscellaneous product but when the data guides you in a wrong way those tiny things shoots up the revenue resulting in the high cost of the job.
9. Directionless company march
Updated data will give the right information that will lead the company in a specific direction that is in the trend but poor data will lead the company nowhere and it will come to a steady growth which in turn makes the company directionless.
10. Inputs to Empower fail
The human resource will get all the data from the senior most panel in the company who directs the and workforce but poor data leads to wrong inputs that will in-turn give worst work notes to them and these notes will be having no substance to lift process or company itself.
11. Business intelligence at a stake
An appropriate data will get let the business have a great strike rate across the organization; internally or externally, but when the data itself is dirty the minds working them shall be acting bad and so the business intelligence will be at stake.
12. Paying for Errors
Poor Data Quality is like having an insensitive and inappropriate tour guide that takes you across all the places which you are hardly interested to visit, and yet paying for it. The dirty data will end up digging in for no substantial search and still, you will have to pay for the work being done.
13. Compatibility of ill data with the upcoming requirements ends
Business is all about trying and testing new things with the help of all the data available. An Ill data will have not horizon that can connect with the current environmental needs as it itself is of no use and hence the compatibility of the same with new needs comes to an end.
14. Bridge breaks between the customer and company
Business is not just about earning finances but it is to serve the needs of the society as well; when the company keeps on nurturing poor data, the need of the end consumer will not be full filled the bridge will break between both of them.
15. The decrease in the company’s valuation in the market
Who would like to invest in a company who deals with low quality of data and get the adverse outcome for the company itself; When the company is not taking care and apt steps for its betterment; would the stakeholders feel secured to invest in such company? And the answer is No!
16. Data Asset becomes data liability
The company recruits an executive to take care of data that is mined for the organization. Now if the data is not apt and upgraded it will be of no use to the company and in-turn the data which is the asset will convert itself as a liability to the company as the company bears the cost of maintaining it by an executive and other such expenses.
17. Stressful work environment
All the input that the company suggests to its workforce is exercised but though due to the wrong encompassment of the strategy laid by the top management leads the organization to generate low income and hence those who work are under constant stressful environment leading no-where.
18. Lack of expertise
When you don’t have apt material to process the success on you end up with the production of things that a customer will not even think to entertain for a while. Thus wrong data will let the organization off the edge from the market.
19. Reduces sales
Being on battle with the most outdated apparatus will let you lose no matter how confident you are. Hence the production, however, stands to be of no use to the end customer; will lead to a reduction of sales.
20. Workforce reduction
A wrong strategy will lead the company in the vague direction, this will end up in no productive work and when a company is not productive it earns no bread and butter for the workers resulting in workforce reduction.
21. Gets no financial aid
When the company feels challenging in understanding the requirement of the customer it ends up with low sales and least turnover. When these things are sensed by the financial organization they would stop lending loans to the company.
22. No department bears responsibility
With Poor Data Quality the management leads cannot hook the fault on any individual department as the problem is with data and not with the department.
23. Sensible knowledge at stake
Those companies who have got sensible brains at work will deal with dead data resulting in worst outcomes; these sensible knowledge owner’s skill will be of no productive use for the company.
24. The life span of the company will near an end, Soon!
The base of the organization is its data and how you deal with it, but dead data will get you no live results and over a period of time the data will pull the company towards its dead end.
25. Increases cost of the product
The company when works as per data provided help the all the department to reduces its cost and that shall help in lowering the end consumable product served by the company.
26. Sumps up errors
Ill data gives you a park full of errors. Now not maintaining it gives the company addition of error over the period of time and hence it piles up the bunch of errors which are dreadful for the company’s future existence.
27. Innovation hardly exists
All you need is a great support to innovate things for your company. No one in the universe is able to innovate without the sound quality of data. These data will not let the company have innovation and will work on the same old school pattern giving nothing good to society.
28. Employee loses confidence
If the work of the employees is not acknowledged in the company or but the outside world it gets him to low morale and the performance; as a consequence to which deteriorate gradually.
29. Bad marketing of the goods
The marketing activity in any organization works on strategy. When, what, where, how, how much is answered by the data in the organization. Hence if the marketing team is not fed with nutritious data; the department will end up having poor health in terms of performance.
30. Relationship with the outsiders and customers feels challenging
In business, the relationship with the third party is as important as within the organization. If you have the wrong data of the stakeholders then to maintain the regular needs will be challenging.
31. No value addition
Importance of data in the organization is because it aids into the decision-making process but dirty data will add no value in the decision being taken into the organization.
32. Loss of important information
A series and sequence of a number of important events of the company with the people associated with them need to be present in the data for various future needs. If the data is ill it will not serve any purpose and important information as per need and loss of important information will lead to a huge loss to the company.
33. Bump in the way when dealing with Bulk quantity- Inaccurate data
All you need is some basic data on which you can work on for bulk operation. Inaccurate data holds the way of the company when they are about to deal with tonnes of it.
34. Quality can never be promised
The unfurnished part of the data will never let the company give cent on cent on quality. Hence with such bad data, no company can give a promise to deliver a good quality of data.
35. The constant fear of authentication
Those brains engaged with the organization are far rational than data at times. When you share these data they will rationalize and wear a cap of doubt always with which those leads placing the data in front of the executives will have low confidence of authentication of data.
36. Uniqueness will never be a part of the product
To top the charts you need to have some unique additions so that the end users can feel a direct connect for the things they consume but the obsolete data will never give you the right indication of requirement and hence no uniqueness will be brought in by the company.
37. Partial facts presented
When the company is presenting the core fact for further planning; the chair will have a look at only partial facts being presented by unmaintained data. Dealing with the same partial data will lead to the suicide of the further management process.
38. Wrong evaluation of raw materials
The data being old will have an older valuation of the raw material and because of this the profit margin or the correct cost price will never be brought into the picture. This will lead to a reduction in profit on a longer run.
39. Desperate time NO MEASURES
Data are very vital in the time of crises. When the company has a great data back up, they can plan a new plot and stand still in the market but in such desperate times when a company has dirty data they will end up with no tools to fight.
40. Difficulty in maintaining the Meta Data
The basic set of the data known as Meta Data or the parent data; which is very essential to record the growth of the company over years will face a problem with the constant dump of ill data along with it, as then no right figures will be presented thereon.
41. Misleading statistics
The data that the company holds is to gain support for future but with bad data, the statistics that are presented in the meeting will surely be misleading and if the decision pertaining to it is taken; it will not serve the exact future needs.
42. Wastage of planning and execution
On the basis of data that is presented; if the company starts acting on it; a sure shot blunder in the planning and execution will be witnessed; as a strong building needs great pillar a great execution needs great data.
43. Loss of important case studies of previous episodes
The comparison of data gives various hints that have happened over a long period of time. A mismanaged data will lead the company towards failure in learning the important scenes that occurred on various occasions
44. Witnessing the growth of other company in the same fraternity
Over a period of time, once Poor Data Quality starts shadowing its eclipse, the management will see a rainbow of growth in competitor’s revenue.
45. Wrong interpretation while doing the SWOT analysis
While doing the most important analysis of the company that is analyzing Strength opportunity weakness and Threat Company needs important data, if it does not have, it will fail in the basic analysis which will lead to failure.
46. Pageant of the poor organization will be pronounced
Those organization which deals with obsolete data will not be able to find the a way out in adversity and hence they will be under the banner of poor organization.
47. Less data relevance and no security
Success only comes when you have a straight, smart and right data to work on. When you work on data which is not upgraded or not maintained appropriately it will lead you to an unsecured environment which will not lead to success at any cost.
48. Start from the scratch cost
It has been observed that the organization who do not work on maintaining its basic asset- ‘the data’ understand the value of it at a long run post which to start again with a scratch takes him to an expensive ride of cost.
49. Results in paying fines
When the data are not updated and the customers are approached violating the rules of TRAI the company has to get ready for the memo being presented by such supreme authorities.
50. Data Integrity is doubted
Data with integrity stands for data with a complete set of requirements. Since the data are not managed over a period of time the company has a skeptical view on its existence and reliability.
In short, poor data quality opens the door to a lot of troubles. It can lead to small losses or end up ending the reigns of a successful business for good. Companies should understand the perils of bad data and should invest heavily in technologies that can help them get access to good quality data. It should be noted that companies might feel investing in technologies as an additional overhead cost, but they fail to understand that investing in technologies give returns many times over and save them from becoming irrelevant in the long run. So, it is better to avoid poor data quality at all costs, and this is only possible by being proactive and investing in the right technologies at the right time.