For Business Intelligence (BI) to be an accurate driver of analytics-led success, the master data must be well organized and consistent and include all different master data being used across all enterprise departments.

Master data is the root data that is kept of any particular entity or process. This data does not change with every transaction and, hence, is most suitable to be used for all key searches. It could be viewed as non-transactional or foundational data. Examples of master data could be the details of customers, vendor details, etc.

Though BI amalgamates all data, including transactional data for analysis and processing, the uniformity and normalization of all data-led processes must start with the master data. Thus, the management of master data is crucial for the accuracy and consistency of the analysis undertaken, the queries raised, the dashboards created, graphs, reporting, etc.

Care should always be taken so that the master data is normalized and up to date across all enterprise departments. Services of data management consultants are often used in an enterprise to satisfy the priority that needs to be given to this process while accounting for the complexities involved.

For the purpose of this article, let’s look at the mistakes that hurt BI projects and could be addressed with better master data management.

1. The Highest Level of Management are Bystanders

Any BI project needs master data management (MDM) as one of its primary activities. Normalizing master data involves every single department and business process in an enterprise, including key personnel managing that process.

Broad-based decisions, both technical and business-based, need to be taken, compliances have to be defined, and then the masters need to be built. Customer data, vendor data, sales and marketing data, inventory classifications, and supply chain categorization are just some of the criteria whose master data have to be derived.

Deriving this master data requires the input of employees, shop floor managers, engineering, IT, vendors, and many other entities of an enterprise. The amalgamation of this input can only be done successfully with the involvement of top management.

Data from various entities across the organization must be studied, analyzed, and then shortlisted as qualified master data. However, final inclusion in the master requires input and approval from the highest authorities of the enterprise, as everything hereafter depends on this exercise.

So, the top-level management staying a bystander in the MDM of a BI project will ensure a certain failure of the whole initiative. They have to be active participants and drivers in the process.

2. Business Case Failure, Funding Contention, Scope Underestimation

“Why” the enterprise chooses to implement a BI project which entails MDM is a crucial question that needs to be answered at the very onset of such an initiative. A clear business case needs to be established regarding the need for this exercise. If the company at all levels is not convinced of the basic need of BI and MDM, the business case will fail sooner or later.

A lack of realistic business case understanding of the advantage of a BI initiative along with MDM within management could also lead to priority and funding contention. A lack of priority given to the exercise at all levels, including adequate funding to ensure continuous and consistent implementation, could hamper and lead to the failure of the initiative.

The business case has to be convincing enough to ensure the required priority and funding are maintained across the entire life cycle and beyond of an initiative such as this. Even if the business case is accepted and given due importance in terms of priority, management attention, and funding, underestimation of the project’s scope can lead to failure.

A detailed plan of scope, estimations of duration, milestones, and other such criteria must be defined and planned, which could ultimately lead to a thorough understanding of the scope of the BI project. BI experts can be roped in at this stage to assist in the building of the case as well as its scope.

3. Lack of Governance and Data Mismanagement

For MDM to succeed along with the BI project, data quality and consistency are crucial. All business case efforts are built on the foundation of good clean data – both master and transactional. The data needs to be the latest; it needs to be accessible, synced, and organized. Failure of basic data sanitization will fail all MDM and BI efforts.

Data governance and data management are processes that must be introduced in the initial stages of the digital transformation process. Any business case for a BI project along with MDM has to be built on compliance and protocols. All data across the enterprise has to be normalized and available. The data has to be of good quality; it must be updated, and it should be sanitized enough to be used for business process decisions.

Data governance and best practices in data management have to be mandated. Vendors specializing in data governance are often contracted for the same said purpose.

4. Technology For Technology’s Sake

There is a lot of hype around technology tools available for MDM and BI. Sales and marketing teams of companies selling solutions for the MDM and BI are in overdrive, trying to take full advantage of the digital transformation age. However, an incorrect selection of software tools and solutions can break the fundamental of the business case itself.

Due diligence and careful selection of technologies that will be used must be carried out. Is the technology appropriate for the business case of the company? Is it scalable and interoperable? Is it affordable? Is it reasonably easy to use? These are just some of the questions that need to be answered.

5. Where Are the Milestones?

BI projects with MDM have scopes that can last across years. The cost-benefit of such exercises takes time to show. The actual productivity and decision-making benefits of BI projects, along with their MDM components, are a crawl initially and can be viewed as discouraging signs of worthwhile ROI in the initial stages.

Appropriate milestones need to be set and monitored so that the actual progress on the business case is quantifiable. Once progress can be tracked with some kind of metrics, all parties involved can be updated, and corporate stakeholders can see results quickly.

Periodic assessments are needed to ensure the project is on track and ROI is being taken care of at all times. The impact of MDM and BI needs to be made visible across the entire enterprise right from the project’s inception, and this can be done only with milestone and metric measurement.

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