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Perfect data quality lays the foundation for successful spend controls.


The present era businesses manage huge volumes of data, with the amount of data maintained, controlled, or utilized by any organizations increasing exponentially in a very short time. Enterprises strive to match the pace of business to the market demands, while continuously leaving no stone unturned to thoroughly manage and preserve their data, further, presenting it more readily available without risking the security.

The lightning speed at which enterprise data is increasing, data classification simply must occur at that pace. Iavenir’s data classification tool, iClass provides a solution to these difficulties and is the underlying element of your entire data security strategy. iClass automatically detects, classifies, and follows sensitive data from the time it is generated, transformed, or forwarded.

Why is perfect data quality essential for stringent spend management and controls?

Each successful spend is the result of a long, arduous process. Most large, international corporations, for example, start off by identifying the bundling potential. This means recognizing similar goods and services across all divisions, departments, and companies in order to harmonize them as much as possible.

The main challenge in procurement is cleansing and consolidating this information, which stems from many different sources. Once this data is comparable, it can be presented transparently across the enterprise in a common procurement language (e.g. eClass or UNSPSC).

The complexity of a project like this, however, should not be underestimated. The source data stems from many different systems with their own languages, classification systems, taxonomies, and quality standards (see figure). Numerous data errors as well as price/volume outliers need to be identified and cleaned up as well.

Once the potential material and service bundles have been identified and specified, the next steps are consolidation and clustering. These clusters produce standardization options and larger volumes which reduce the price per unit. In anticipation, procurement professionals inform potential suppliers, request offers from the most qualified of them, and enter several rounds of negotiation with the two or three best suppliers.

Once a successful contract has been signed, many strategic procurement departments view the successful spend as a sure thing. Experience shows, however, that is often not the case. A negotiated umbrella contract is no guarantee all internal customers follow these guidelines. It also does not ensure that the negotiated purchase request volume suffices over a longer period of time. It is not uncommon for suppliers to ignore the negotiated prices and processes. This is one reason why companies generally do not generate the savings that they anticipated.


Which employees are buying which goods and services at what conditions worldwide? This question is decisive, but the data quality in most information systems is too poor to provide any insights. Sophisticated data cleansing processes (e.g. price and volume outliers, false exchange rate conversions, or units of quantity) are necessary to find the answers.


If master data comes from different (heterogeneous) data sources, it has to be consolidated, clustered and brought into a hierarchy. Thereby, e.g. Identify supplier duplicates and group similar suppliers. A clustering of similar material master records is the prerequisite for standardization etc.


Master data clustering is one criterion for perfect transparency. Another is classifying as many invoices, orders, etc. as possible. Orpheus Data Categorize uses semi-automated classification methods to assign these and other types of
transnational data to harmonized spend categories. This ensures comparability and transparency in strategic procurement.