The fundamental bedrock for any analytics project is good data. However, given the data we are analysing is sourced from enterprise transactional platforms, it is often poor in quality, missing altogether and may need to be categorised and enriched.
Some typical causes:
1. Entry quality: Did the information enter the system correctly at the origin?
2. Process quality: Was the integrity of the information maintained during processing through the system?
3. Identification quality: Are two similar objects identified correctly to be the same or different?
4. Integration quality: Is all the known information about an object integrated to the point of providing an accurate representation of the object?
5. Ageing quality: Has enough time passed that the validity of the information can no longer be trusted?
6. Organisational quality: Can the same information be reconciled between two systems based on the way the organisation constructs and views the data?
7. Categorisation accuracy: Is the data accurately categorised to the taxonomy? Is all the relevant data categorised to the taxonomy to prevent a partial view being delivered?
The answer lies in the difficulty of truly understanding what good data is and in quantifying the cost of bad data. It isn't always understood why or how to correct this problem because poor data quality presents itself in so many ways. We plug one hole only to find more problems elsewhere. If we can better understand and identify the gaps in our data quality then we can develop a plan of action to address the problem that is both proactive and strategic.
Each instance of a quality issue used to present non trivial challenges due to the complex and inflexible technologies that store this data but also the matrix of stakeholders that are partially responsible but not ultimately responsible for its quality. Often business points to IT as being responsible for data quality but is in this new data driven world looking weak. The business is increasingly, and should be, accountable and therefore being able to easily and effectively quantifying the issues in a business context is important in order to determine where our efforts should be focused first.
We have designed a new way for the business to quickly and effectively strategically assess and manage their spend data.
The first step to developing a data strategy is to identify where quality problems exist. These issues are not always apparent, and it is important to develop methods for detection. A thorough approach requires inventorying the spend data, documenting the business and technical rules that affect data quality, and conducting data profiling and scoring activities that give us insight in the extent of the issues.
Once you have established a baseline, it is important to assess the business impact and cost to the organisation of the gaps and from this prioritise the focus. The downstream effects are not always easy to quantify as the cost associated with a particular issue may be small at a departmental level but much greater when viewed across the entire enterprise. For instance, the procurement team may not immediately see the benefits of enriching the vendor master file with longitude and latitude coordinates but the supply chain and risk teams will reap huge benefits.
Addressing data quality used to require changes in the way we conduct our business and in our technology framework. It requires organisational commitment and long-term vision. The RAPid platform brings down these barriers, reduces complexity and put the powerful data technologies directly into the hands of the business user.
They will enable you to drive new and exciting levels of commercial insight across the enterprise.
These important tools we have developed are a blend of analysis, technology, and business involvement. When viewed from this perspective we are delivering you a pre-configured framework that identifies quality problems, scores data value and delivers you a structured easy to use framework that will accelerate the information that your decision makers are receiving.
The end result is that we genuinely believe that there will be few other teams in the organisation where attention to quality and excellence can be matched.
It could be construed by some as being a rather dull topic so we are increasingly employing gamification techniques. These high vision tracking and scoring dashboards turn a “dull topic” into a game that everyone can influence and track. Whilst enabling the management report up and manage down in an easy interesting and engaging manner.
Discover new sources of value for the wider business by reevaluating your procurement data and processes.
This free model, designed by procurement experts, will help you to quickly and concisely identify the next steps for your data strategy.