Data consolidation is an effective approach for understanding spending trends and behaviours in the largest of organisations, and can be used as a tool to negotiate better discounts and deals with suppliers.
According to The Data Warehouse Institute, the most prevalent driver of a data consolidation project is the need for consistent data across the organisation, and almost 90% of respondents to a survey rated this as "high" or "very high". Some 71% followed this with reducing costs and overheads, while 50% wanted to standardise the IT portfolio.
That said, the survey found that only 11% of organisations have completed an analytic silo consolidation project, while 56% are designing or implementing a consolidation project and 26% are exploring the idea. Seven percent have no plans.
Therefore, most organisations need to ensure they have clearly predefined goals and objectives for their data consolidation project if there is to be a return on investment, or they risk spending a lot of money and time achieving little.
Recent decades have seen globalisation of businesses looking to increase profits. Integration and systems standardisation have lagged behind this. In addition, the Internet, for one, opened many channels for data collection and the amount of data businesses collected has ballooned.
In theory, the solution is simple, but in practice many organisations are falling prey to some of the most common pitfalls of data consolidation.
Charl Barnard, GM: business intelligence, KID
Recent years have seen the standardisation and possible integration of many core systems with the ability to produce a higher quality end product. Internet standards have also been used to integrate systems for data exchange purposes.
Organisations are using extraction, transformation and loading (ETL) tools to transfer data into data warehouses or data marts, followed by bolting a business intelligence (BI) tool onto the front of that and analysing the collated and aggregated data. That analysis leads to a number of business benefits, allowing businesses to use IT to tap into corporate knowledge, improve internal processes and facilitate workflow and workforce collaboration. This ultimately delivers knowledge to the business users who need it most, prying it free of the hands of IT technicians.
In theory, the solution is simple, but in practice many organisations are falling prey to some of the most common pitfalls of data consolidation:
* Poor pre-planning
* Lack of predetermined goals and objectives
* Failure to establish the transformation team
* Lack of quality assurance
* Language barriers between countries
* Inflexibility
* Failure to choose the required technology
According to a recent survey conducted by The Data Warehouse Institute, organisations have an average of two data warehouses and six independent data marts still to consolidate, and only one-third of the data structures have been consolidated.
Right up-front, it is critical to ascertain why the data is to be consolidated. For instance, cost savings and centralised buying initiatives are often drivers behind data consolidation and analysis projects. Regardless of the scale of these initiatives, ensuring project success is easier when the following points are considered:
* Executive sponsorship
* Solid team experience
* Multiple geography experience
By far the most critical success factor is executive sponsorship. If the data consolidation is being performed by a multinational organisation, then executives from all regions must be part of the buy-in to the project. Not supporting the project will derail consolidation efforts through a disjointed and non-standard approach.
Overall, the project can be used to identify cost-saving potential in procurement and many other areas of the business; it is ideal for companies to engage the services of a third-party organisation with the necessary expertise, at least to develop the plan, even if the corporation retains a large in-house IT department.
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