People like to blame data, but business issues due to underlying data problems speak to wider concerns about how data is valued and managed. Is the data in your company regarded as infrastructure? If not, then some of the following may be business challenges your organisation will experience.
|Data breach.||A data breach is indicative of failing information governance, security and curation. Breach notification (and remediation) are statutory reporting obligations to the ICO, and can affect future insurability of the company in breach, but also the company officer in breach.|
|Unreliable decisions.||Decisions are adversely impacted by relying on analysis of poor quality data, generally estimated to be about 80% of organisational data. Unless data is curated, you should expect it to decay at a rate of 15% or more per year, and that does not include the estimated 20% of erroneous data that organisations typically have. Like everything else in life, data changes.|
|Demotivated workforce.||Personnel become very demotivated when the data they need to do their jobs is unavailable, inaccurate or unreliable. Inaccurate, duplicate or inconsistently-parsed contact information can be highly embarrassing, costly and cause long-term PR & reputational damage.|
|Slow reports.||It is not uncommon to find that reports take ages to run (overnight) or have to be ordered far in advance, even after ritual abasement. Decision-making is also adversely impacted by lack of timely analysis and reports.|
|Competition always get there first.||Strategic opportunities slip from grasp, as the organisation is always last to the party. Proactivity is only possible with command of data which, when converted to information, provides insight.|
|Legacy costs.||You could be paying as much as 80% over the odds for the hidden cost of data you don’t need, don’t understand or should at least have archived. Data should be an asset and you should be investing in the future, not the past. Each year, UK businesses waste £220m sending mail to the wrong people!|
|Spreadsheet anarchy.||Business processes fail as data is compartmentalised; held on desktops instead of being available to the rest of the organisation where it can inform decision-making. Spreadsheets were designed for the desktop, not the enterprise, and in this instance, are over-used as a departmental tool.|
|Squirrelware on desktops.||For various reasons, employees may store their own spreadsheet versions of data, for reasons of quick access, convencience, fiefdom, or where they lack confidence in corporate data or systems. Enterprise information should not be kept on the desktop, since the presence of squirrelware leads to data protection and other vulnerabilities and causes severe headaches for a DPO.|
|Data silos.||Data trapped in silos, individual business units or packaged systems is not available to the rest of the business. This speaks of a greater concern: a reluctance to buy-in to the team approach of the corporate strategy as people or departments become too protective of their systems and data, thus denying it to the rest of the organisation.|
|Data ownership.||When discussing innovation, new projects or initiatives, the business does not know who ‘owns’ the data – and the answer to the question “who owns the data?” is typically “the business!” This hinders business evolution.|
|Low conversion rates.||High quality data demonstrably leads to better conversion rates, through an improved customer experience and operational efficiencies. Poor data quality adversely affects customer insight analytics.|
|Unable to find information.||Poorly organised (maintained and curated) data, and poorly described (metadata) data are the cause of significant organisational overhead. As much as 40% of personnel time can be spent trying to find data or in fruitless searches that do not produce meaningful results. As data grows, hoarding it will become more and more unsatisfactory unless fidelity is also addressed.|
Update: Getting your data straight has now even more immediate consequences as AI/ML & blockchain projects profilerate. In 2018, AI World Cup predictions were an epic fail. Why?
“SQL Services Data Scientist Nick Burns explains: “No matter how good your models are, they are only as good as your data… recent football data just isn’t enough to predict the performance in the World Cup. There’s too much missing information and undefined influences.”