Maintaining pipelines, warehouses and lakes of decentralised, unstrategised, degraded and mothballed data is counter-intuitive to profitability and detrimental to customer service. In this post, we look at what happens when dirty data muddies the tide of business opportunities.
We know that data is the new oil. It’s estimated that by 2025, users will generate 463 exabytes of data daily. But successful organisations are increasingly differentiated by excellence in data quality and ethical stewardship. As data volumes increase, opportunities to monetise data stacks and sets rise exponentially.
But all these boons hinge on the quality of data and how it is collected, stored, documented, managed, and made accessible throughout corporations. And whether or not it is seen by the C-suite, developers, coders, data engineers and analysts as a company asset.
Quality data drives the application of advanced analytics to improve outcomes; it helps leaders and data scientists to implement continuous improvement measures across their decision-making processes. Quality data is validated, enriched and reconciled across multiple sources, reducing operational risk.
Big data refers to the amount of data, structured and unstructured, produced by an enterprise in the course of a trading day. The majority of big data is unstructured – text-heavy, non-numeric and quantitative, requiring data wrangling. Structured data fits into a predefined data model. It has defined data types and rules for processing, is documented and labelled, and is easy to extract information from. A real-world example of structured versus unstructured data is the date and time of an email (structured data) versus the content of the email itself (unstructured data).
Master data is the primary wellspring of info across the entire organisation, so it stands to reason that incongruities in master data have a negative domino effect of chain reactions, including having to spend time and resources on damage control and root-cause analysis.
Making decisions based on grubby data in reports and on dashboards is doomed to fail, compounding the cycle of bad decision-making and hurting the bottom line.
Covered In This Article:
The Causes Of Dirty Data
The gradual corruption of data within platforms, databases and systems – data decay – and even bit rot is a real problem and the dangers of silent corruption cannot be underestimated. Typically, customer records decay at 25% yearly, hindering business operations and production lines.
The direct cost of bad data can be measured in sales losses and a downtick in customer uptake of your products and services But what about the indirect cost? These may include inaccurate customer and market segmentation, loss of employee productivity and morale, and a surge in operational expense and reputational damage.
Global data privacy regulations require strict adherence to best practice data security and confidentiality. Companies can be fined for misuse of data. Customer confidence is directly related to how their information is used (or abused). Bad data impedes the customer experience – running the gamut of customer fury at being sent the wrong item to data breaches that threaten national security.
Other examples of dirty data disasters include credit scores which contain flawed data scraped from a consumer’s retail and social media behaviour, then sold to credit scorers by data brokers who have little interest in or recourse to correct their mistakes. These consumers may suffer the consequences of algorithmic bias and blacklisting, with the compromised credit agencies and zero clue of how they got there in the first place.
These issues spell out the need for data management. Successful data management leverages data governance, quality, transparency, integration, dissemination, deployment and provenance to create a single version of business truth in an audited and regulated metadata environment, eliminating data silos and fostering a culture of data ownership and good governance.
Data exhaust is the by-product of data emitted from existing systems – passive data traces, which are potentially mineable – and data mining dark data may offer hidden benefits. But what happens when you throw good data after bad – when bad data remains, well, bad?
The Cost Of Dirty Data
In a word – loss. The result? A compromised bottom line.
Not to mention malformed content – corrupt data values, such as data entry mistakes; missing engagement points like incorrect phone numbers, causing customer churn; and unnecessary data, for example, irritated clients entering fake info in overlong website forms – all these bogs down and sabotage your sales and marketing strategies.
Multiple studies from big-ticket survey companies cite major losses to companies as a result of poor-quality data. One study reported that some companies couldn’t measure their deficits because they didn’t quantify bad data from the get-go. Another study stated that only a minuscule amount of data was analysed and used in the first place. This goes to the need for quality data to be accessible so that businesses can reap the rewards, not suffer losses.
Why Decentralised Data Hurts Your Business
When data is decentralised, fragmented and incompatible, and occupies multiple endpoints, your data team is throttled in its ability to find solutions to problems. Innovation is stymied and your business loses its competitive edge.
Optimising workflows and automating several web apps may be served by point-to-point connections, but this methodology of data management does not allow you to derive the maximum value from your datasets. Centralised business intelligence tools are placed in a prime position to make the best data-driven decisions.
Although merging silos through data integration isn’t the proverbial walk in the park, data lakes and warehouses are a rich source of strategic advantage for companies wanting to use their datasets as a strategic advantage.
Data generated from several different sources, left raw and disorganised, are a hazard to asset management, as well as a waste of time as business decisions take longer to make. When employees keep using their own version of the same spreadsheet, database or customer record, it is almost impossible to centralise updates, minimise duplication of effort and keep overall records up to date.
In a similar vein, marketing campaigns within the same company, rendered decentralised and inconsistent by low-quality data, make for mixed levels of content quality. Bumpy customer journeys may make clients want to terminate the ride. Unprocessed, dirty data, under-analysed and stored in multiple locations, makes organising and retrieving it a messy process.
By contrast, centralised data stored in a data repository is an open invitation to business intelligence and marketing teams to reap the benefits of profound insights into business operations and processes. From networks to supply chains, the lens through which forecasts and predictions are made becomes crystal clear and troubleshooting is a breeze.
Savvy digital natives and customers with high brand expectations clock business irregularities in a millisecond. When data is decentralised, that’s inevitable. However, when data is centralised, standardised high-quality communication and enhanced customer experience is the order of the day. And reporting times are significantly reduced because there is no longer the need to generate multiple reports from different platforms. This makes making data visualisation – a view of the bigger picture − second nature.
Also, it bears mentioning that centralised data comes with the benefit of backup depositories which can be automated to create and export data in different formats for different platforms. That data can then be leveraged and made into data dumps to be pooled with other data streams.
Effects Of A Lack Of Data Strategy On AI And ML
Artificial intelligence (AI) and machine learning (ML) are powered by quality data, but poor data inputs are the main reason for shelving AI projects and clogging ML pipelines. One thing is clear: unclean data robs industrialised states of a significant portion of their GDP when its private sector suffers reduced productivity, pipeline debt, system breaches, data migration slowdown and a rise in maintenance spending.
Lack of data, incomplete data and limited or no access to data are results of company data mismanagement and the degree (or not) to which data is accessible to every company stakeholder. A poor data strategy hampers AI in the following ways:
- Impeding exploratory analysis of data, either by blocking the exploring of the data’s possibilities or the decision on whether AI can solve a particular problem. Either way, you need the right data to spot data imbalance and sparsity.
- Without a centralised data pool, companies rely on data dumps, which may not be reflective of the present moment, known as “stale recommendations” or “stale predictions”, based on outdated data.
- Stale predictions refer to predictions “learned” from outdated historical data or data that does not reflect current reality. Remember: customer, government and cultural behaviour changes over time, as does data distribution.
- Low-quality models with poor accuracy often predict and recommend the wrong thing because of decentralised data, or they can only access data subsets, or the data volume is insufficient.
- With access to only incomplete, limited or decentralised data, data modellers and scientists are unable to deliver an adequate representation of reality, eg. ML racial bias skewed in favour of white males in facial recognition software.
Companies without a comprehensive, cohesive and centralised data strategy risk negative and costly impacts. After all, business decisions informed by good quality data mitigate against risk and transform raw data into rich veins of data gold, there to be mined.
In the next article, we will take a look at how to solve the issues mentioned above so make sure you are subscribed to our CRM mailing list by following this link.