All organisations invariably come across their unique set of data-related challenges. Nonetheless, there are 10 common themes that data professionals generally see across various organisations, which might not be readily apparent to ‘non-data’ leaders:
- Over the past 13 years, hiring a team of data scientists has become fashionable. However, experience has shown that in most organisations, these data scientists spend 80% of their time wrangling data and only 20% of their time delivering insights and value. To prevent their valuable efforts from being squandered and avoid employee attrition due to frustration and inefficiency, ensure your data is in decent shape.
- Business comes in many forms and sizes. Excelling in your primary product or service offering doesn’t necessarily imply proficiency in handling data. Specialised experts in data management exist and are often oblivious to specific core operations. Business leaders should recognise this gap and recruit professionals to counterbalance their limitations.
- When listing assets, we often overlook one paramount offering of this digital age – Data. If given a value, data’s worth could be a staggering figure dwarfing other assets. Consider this: Could operations get by after 10% staff cut? Likely, albeit with challenges. However, losing 10% of all data records would immediately halt pivotal operations like order fulfilment. Viewing data as the precious asset it is motivates businesses to dedicate resources towards its upkeep and enrichment.
- Data demands quality, not quantity. Just as the finance team knows the current bank balance and the procurement team remains aware of ongoing supplier negotiations, the data team should ascertain the volume, quality, and consistency of the data generated each month. Thorough monitoring propels effective management, making data quality monitoring fundamental for driving better business outcomes.
- With the evolving data landscape comes a plethora of solutions promising to cure all data woes. Avoid the allure of ‘silver bullet’ solutions and partners who recommend them. Remember that technology addresses only 25% of data issues and needs to work in tandem with the transformation of people, processes, and governance structures.
- For almost 30 years, Excel has been the go-to tool for quick data analysis, acting as a scratch pad for rapidly testing hypotheses. However, Excel was never designed as a holistic data management utility. For sophisticated analysis and long-term sustainability, businesses should steer towards robust, dedicated data systems.
- Data engineering is complex. It involves the intricate process of collecting, validating, storing, protecting, and processing data to ensure its accessibility, reliability, and timeliness. Deriving value from it requires expertise, resources, and time.
- Though the introduction of a new data warehouse might seem appealing, it often fails to resolve underlying data quality issues. Typically, it exacerbates existing problems by introducing another layer to the puzzle.
- Data sharing has become as vital as the delivery of physical goods or services. Suppliers must be contractually obligated to provide data in a predetermined format and on a specified timeline. If non-compliant, clear and enforced penalties should be in place. Such proactivity prevents the erosion of valuable data and the waste of resources on managing unacceptable supplier data.
- Now a key element on most business roadmaps, sustainability initiatives may illuminate more subtle shortcomings in existing data processes, teams, and systems. If current data requires labour-intensive manual manipulation, prepare to replicate this effort for sustainability-related data.
With these principles at heart, any organisation can make strides towards an enlightened data management approach and revitalise their operational strategies.
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