The lockdown approach to COVID-19 has accelerated existing digital transformation efforts. Yet, according to a Forbes article, many companies hit two immediate, potentially crippling problems.
Both are related to data.
The first has to do with the way companies have historically stored and accessed data; the second involves the quality of their data.
Unless addressed, these problems impact both the ability to operationalise digital – roll out new channels, products and communication strategies – and analyse the data.
Data Integrity goes beyond data quality to deliver consistent and accurate data across all the organisation’s information systems.
Data integrity is critical for the successful delivery of AI decision making and the digital enterprise
The Four Pillars of Data Integrity
- Data Integration
We live in a highly connected world where business units and even business partners share information on customers, products, locations and much more. Even within a single company, however, however data is often replicated across functional silos – for example, customer data may exist in both the CRM and the ERP system.
Variations in how siloed data is dealt with results in inconsistencies and duplicates within and across departments. Aggregating data, for example for reporting, without a clear data integrity strategy amplifies the problem.
Some market surveys indicate 68% of organizations say disparate data negatively impacts their organizations.
A data integration approach that identifies and updates related records according to a set standard is essential to to shift from functional-level projects to a full digital transformation.
2. Accuracy and consistency at scale
The complexity of modern data management can be tied to both the sheer (and increasing) number of systems in play, and also to the volume of data being generated. Advances in storage and computing capabilities enable the collection of vast amounts of data, for example from IoT devices, whilst distributing data both on-premise and in the cloud.
In turn, these data sets of of interest to data scientists and data analysts, driving mainstream adoption of advanced analytics techniques, including artificial intelligence and machine learning. Modern analytics platforms are capable of consuming vast amounts of data, automatically analyzing it, and deriving insights that drive efficiency and innovation.
AI and machine learning bring new data quality challenges.
Experience is already showing that advanced analytics are highly dependent on accurate, consistent and unbiased data. Early adopters of these advanced technologies have discovered than analytics and AI can do more harm than good, unless the input to these tools is clean and reliable.
It is not enough to know what data you have. You must also begin to understand what data you are missing.
A sound data integrity strategy must be capable of managing and validating data across multiple systems, identifying gaps or discrepancies, and triggering workflows and processes to correct those errors.
3. Location Intelligence
Location intelligence involves the use of geospatial data to reduce risk, better understand customer behavior, and increase efficiencies.
COVID-19 has driven shifts in both when and where consumers shop. Without a proper understanding of these shifts businesses cannot make informed decisions about where to retain, and even extend, physical locations (including both retail outlets and distribution centres), and where to shut down.
Location intelligence should be a key component of any overall digital strategy.
4. Data enrichment
This leads us nicely to the final pillar of data integrity: enrichment.
If we agree that our data is a business asset then it makes sense to invest in enhancing the value of that asset by adding trusted, third party data.
In the context of data integrity we may want to add insight and context to our internally curated data. For example, we may want to understand the demographics of the area in which our customer lives, to improve our marketing message.
Smart business leaders are blending trusted, third party data with internal data to uncover new opportunities.
Understanding that data is a strategic corporate asset, smart business leaders are establishing clear frameworks for implementing these four pillars of data integrity. They understand that reliable, secure interconnectivity (data integration) is a clear starting point. They also understand that meaningful insights cannot be derived from data that is inaccurate or incomplete (data quality). They understand the power of location (location intelligence) and the potential to create additional value by integrating data from third-party sources (data enrichment).