4) Directly address credit crunch sensibilities. BI endeavours can be expensive and challenging to articulate in terms of benefit. You may need to plan a BI strategy; but right now, projects need to have a critical-mass of internal support and short-term benefits in order to obtain funding and/or avoid being postponed. Key areas are:
a. Cash flow management. Cash is King. Cash flow metrics e.g. Price to Cash Flow/Free Cash Flow enable managers and potential buyers to see basically how much cash an organisation can generate. Data mining can predict cash flow problems e.g. bad debt to allow recovery and/or credit arrangements to be made in a timely (and typically cheaper) manner.
b. Business planning capabilities. While information systems are in place for many organisations to generate representative management information, the level of manual intervention needed to deliver essential reporting can result in unacceptable delay and therefore data latency and inconsistency. This directly impacts the organisations ability to predict and respond to threats to its operations (many - in an adverse climate). Outcomes can be financial penalties, operational inefficiencies and lower than desired customer satisfaction. Building habitual Performance Management (PM) cycles of Monitoring (What happened? /what is happening?), Analysing (Why?) and Planning (What will happen? /what do I want to happen?) - places the focus back on results as well as affording a host of additional benefits.
5) Release something early. Unlike a couple years ago, you likely cannot now go through a six-month analysis and following data cleansing and integration phase. This work does not deliver tangible business benefits in the short-term. Instead, look at getting basic BI capabilities out within weeks. This will allow you to incrementally build upon your successes, gain business/operations experience (through monitoring usage) and build user alliances gradually. How much you can do here depends on your chosen BI platform but building your BI/PM on-top of existing reports (data pre-sourced/cleaned), selectively using in-memory analytics tools (no need for ETL) and SaaS BI; if your immediate needs are relatively modest, all should be seriously considered.
6) Avoid the metadata conundrum. Metadata is undoubtedly important. It is well known to assure adoption; convincing those making decisions (from the system) that they are using the best data available BUT it is a complex problem and intersects other disciplines e.g. data integration, information management and search. Most data objects, whether Word files, Excel files, blog postings, tweets, XML, relational databases, text files, HTML files, registry files, LDAPs, Outlook etc. can be expressed relationally i.e. they make at least some sense in a tabular format. They also span the spectrum of metadata complexity. The end-game is to build on all the ideas of ODBC and JDBC to provide the same logical interface to all of them. A DBMS or file system can then treat them all the same logical way, as linked databases and extract the metadata, create the entities and relationships in the same way and use the same syntax to interrogate, create, read, write and update them. Tools/theories are evolving but this is rarely achievable in practice. If you can satisfy regulatory requirements with the bare minimum – do it. Just concentrate on data lineage if you cannot; building as much of a story around the ETL process as possible. Less than 15% of BI users use metadata extensively anyway.
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