Five barriers to success

A businessman jumps between two cliffs to illustrate overcoming rogue data Barriers

Five different ways in which poor data quality is a barrier to success for any organisation.

We have identified five different barriers to success for those working with big data, including businesses which integrate systems between platforms or from a legacy platform, businesses who have to deal with a lot of user generated content (UGC) or who know that their data are dirty, and businesses who sell or lease software. You may choose to build Spotless data quality API solution into your tools to ensure that you are getting data quality which you can trust in or if you need to verify data they have received from a third party, or simply because your data require data integrity throughout their lifecycle. Each of these five barriers may affect the quality of the final product, or of the business which exists to sell said final product, though each of these barriers fundamentally shares the same problem of being caused by dirty or contaminated data which need data validation.

The five barriers are:

1. Skills availability

Not having the skilled staff available to be able to look after your company's data properly. Crowdflower, the data enrichment and mining company, estimates that 60% of data scientists time is spent cleaning data. With small and medium-sized businesses it may simply not be realistic to invest  a high number of employee hours in data cleaning or employing specialized and high earning data scientists yourselves, given the average wage for a data scientist in the USA is $104,000 a year, while at the same time pursuing other important business activities such as marketing and selling your products.

2. Poorly defined business benefits

Having a poor or unclear idea of why quality data would be valuable to your company, and how much time and money data cleansing to ensure data quality would save it. Dirty data cause irregularities, including lost customers, poor service to customers, and time wasted trying to identify and fix data problems manually.

3. Processing large volumes at speed

Not being able to process large volumes of variable data at high-speed; for instance, if your company's data changes completely each week, such as in a TV Listings service, it is not good to have a system in place which takes seven days just to cleanse the data. Using our data quality solution all we clean all but the largest datasets within five minutes or less.

4. Deriving meaningful results

Having data which cannot be interpreted in a meaningful way. A good example of this is management reporting. Too often data can "work" at what it is supposed to do for the customer, but when it comes to creating management reports the data fail to give an accurate idea of what is happening in the company. In the long-term, this poor reporting will undermine the business as the managers will have a false idea of how the business is progressing, which may lead to poor decision making.

5. Unrealistic business expectations

Unrealistic business models of how the big data your company has is going to be as useful as possible to your company. Companies spend a lot of time and effort obtaining the data they need and, in most cases, they understandably have great expectations about the great things which they are going to achieve on their platforms with this data. Yet, if the data are dirty or contaminated, and hence substantially compromised, these expectations are going to be unrealistic and could ultimately lead either to failure or to being beaten in the competitive marketplace by rivals with better quality and more highly organised data.

These five barriers have a common root problem, which is dirty or inaccurate data. We do not underestimate the importance of marketing in selling your company's products, but we also know that the best marketing in the world is not going to sell a bad product, especially if your rivals have a better product. Remember that the product is only as good as the data which underpins it.

The solution, of course, is to make sure that the data in your company have been properly cleansed using our unique Spotless Data data quality API. When your business is the only one among your competitors who has invested in quality data using Spotless Data, your company is going to be the leader in the field.  Be the first among your competitors to use our service, and your company will soon see the cost-benefit results.

You can sign-up using your email address, Facebook, Google or GitHub accounts. Here is a quick link to our FAQ. You can also check out our range of subscription packages and pricing, and try it out with 500Mb of free data cleansing. You can also view our videos on data cleaning an EPG file and data cleaning a genre column which explain how to use our API. If you would like to contact us you can speak to one of our team by pressing on the white square icon with a smile within a blue circle, which you can find in the bottom right-hand corner of any of the web pages on our site.

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If your data quality is an issue or you know that you have known sources of dirty data but your files are just too big, and the problems too numerous to be able to fix manually please do log in and try now