Enterprise Information Management

At the same time, timely information should be available for the various departments like: Finance, Operations, Supply Chain, Marketing, Customer Services, Analytics, Commercial, and Business Development.

Information is also required at different tiers of the organisation like, Operational, Department and Corporate, mainly for running, managing and monitoring the business.  We can add one more important tier in the information pyramid, called Analytics.  This one layer works across the different departments and functions, brings in in-depth understanding on data.

Operational Reports can support operational activities, but can be mostly be supported by the various OLTP reports.  This leaves the reporting at Department level, Corporate and Analytics for the BI arena.

I think, the BI Arena can be classified under 3 categories as:
1. Business Development Area: Campaign management & Promotions fall into this category, customer segmentation, market place etc.,
2. Customer Engagement Area: Complaints, Returns, Refunds fall into this category etc.,
3. Business Reporting: Budgeting, Actuals Analysis, Transaction & Operations monitoring etc., fall into this category

These 3 areas can also be called as categorised as per relative importance.  The first 2 are more or less customer facing, whereas, the third one is internal facing.

Let us take a close look at the kind of data elements that we have to deal with in this Retail BI arena.  We can categorise these data types into Master Data, Transaction Data, Derived Data, and Analysis areas.

Most of the Master data is around Cutomer, Product, Merchandising, Vendor, Employee, Campaigns and promotions. 

Most of the Transactional data is around Sales Baskets, product pricing, discount coupons, returns, refunds, complaints, fulfilment, and inventory / stock.

Some of the Derived data is around customer loyalty, customer segmentation etc.

I think, we can safely call the following to fall under Analysis Areas: Segmentation, market basket analysis, returns analysis, failed deliveries, inventory analysis, fraud analysis, customer service, delivery options, basket & spend analysis, marketing, profitability analysis etc.  The first 2 categories are also covered in this paragraph.

Let us also look at the Business Reporting area.  Most of the retailers operate on periodical reports in this area for their business monitoring.  They use weekly / monthly periodic reports in this space.  It is observed that most of the efforts are spent towards preparation of these reports, albeit manually, at most of the Retail companies.

After understanding the requirement categories and data elements that are involved, let us take a look at the users' convenience in BI.  We can possibly, list these as follows:

1. Should provide standard reports in a scheduled manner
2. Should provide on-demand reports
3. Should provide self-servicing facility for specific user groups
4. Should also provide analysis areas / sandbox areas for specific analysis

Let us also look at some useful architectural guidelines that can help our cause.  They are as:
1. Provide an Integrated information platform that makes actionable information available, and decision making easier for the concerned information consumers
2. Reporting Platform that supports standard reports, ad hoc and analytical reports
3. Platform that can be extended to accommodate Dashboards and Balanced Scorecards, for senior management, if required
4. Provide customer segmentation and any other analytical data required by campaign management, web analytics and personalization applications
5. Provide a sandbox / playpen area for occassional analytics
6. Maintain historical data at the most granular level, so that it can be used for performing any kind of analytics

Meanwhile, let us look at some of the very important applications like Product Induction Applications and Commercial applications, which create new product offerings, help categorise them, and provide cost price information. Retail supermarket chains keep adding / amending new products almost on a daily basis, and also experience cost price fluctuations (albeit in a narrow range) from their vendors / suppliers.

Let us take a look at the last 2 BI categories (before we go on to category 1 for more analysis) namely, Customer Servicing and Business Reporting.  Actually, I feel that both the Customer servicing & Business Reporting fall in the same category of reporting. Now I am not comparing them on importance, but the data management and data arrangement portions only.  Both these BI Categories need information to their respective users on the following items (not an exhaustive list):

Finance figures on: Sales, Margin, Gross margin, contribution, Payroll, Orders, basket size, promotional sales on various dimensions like Target, Actuals, comparison with previous few periods etc.

Customer figures on: Number of new customers added, complaints, refunds, returns, substitution acceptances, product availability, active customers on various dimensions like Target, Actuals, comparison with previous few periods etc.

Fulfilment productivity figures on vans, pickers, pick rates, missing items, items per order on various dimensions like Target, Actuals, comparison with previous few periods etc.

There will be more interesting items such as: System availability, Advertising revenues, stores utilisations, Basket size analysis, average items per basket, product availability, refund summaries, returns summary, substitution acceptance, website statistics, discount coupons summary, Fraud details etc.

Usually, these info on these items is provided to the business users across hierarchies, on a periodical basis.  We also need to understand the fact the business users' information needs keep changing quite frequently, and sometimes they also need more information that what is provided to them through standard reports.  Sometimes, they also demand information on a more frequent basis.  This kind of information availability is easy through proper data management and its arrangement rather than creating complex reports.  Creating an aggregated and dimensional layer will be useful for the business users information needs. Using a ROLAP environment is going to be more useful compared to the MOLAP environment, as it involves too frequent cube refreshes.  Cube refreshes take more process window, and also necessitates historical data storage and processing.

Now, let us take a look at the first BI category "Business Development Area".  This covers items like Campaign management & Promotions, customer segmentations, market basket analysis, etc.  I recommend creation of some bridge tables to the already existing master data dimensions to indicate these analytical divisions.  These bridge tables also prevent frequent updation of the original master data dimensions.  These could also help in understanding the history of changes done to the master data dimensions. Analysis areas need very thorough and expert hands to dissect, analyse and understand data.  Usually, the methods used in these types of analyses keeps changing over a short period of time. Creation of data marts etc., to perform these analysis is usually not recommended, as they will use-up lot of memory and hard disk space.  Usage of Sandbox / playpen areas to perform these analysis is recommended.  These sandboxes / playpens can have minimal persistent data.  These kind of analyses usually require the most granular data.  Hence, historical data is also kept at the most granular level.

Considering handling loads of data on a regular basis, it is always recommended to use BI Appliances.  These appliances use brute force to handle loads of data to provide useful information quickly.