When I read through several blogs in SAP HANA looking for
the business value it can create, I found 2 very informative blogs that help in
identifying business value in the use with HANA.
http://scn.sap.com/community/hana-in-memory/blog/2012/05/07/explore-hana-use-cases-and-be-inspired
http://scn.sap.com/community/hana-in-memory/blog/2012/05/08/quantify-the-business-value-of-sap-hana
What I did not find there or in any other blogs (and maybe I
just missed it) is a linkage between a specific HANA capability and business
value, explaining which capability in SAP HANA creates what type of business
improvement.
In this context, I am also looking for specific business problems only HANA can solve or
which type of deployment of SAP HANA can target certain improvements. Therefore
I want to write this blog. I will develop it over time and I am looking for lots of feedback.
1 Gain new value opportunities through granular
insight.
This use case refers to HANA as a Data Warehouse.
Before HANA it was necessary to aggregate information
for analytical purposes and regular reports produced by the data warehouse. The
key reason why this was done was to manage the performance of the system by
allowing users to execute queries on a much smaller data set than the original
data. The granular data which was extracted from other data sources was processed
and aggregated into OLAP cubes. The way that the aggregation was designed was
with certain reports in mind that were executed on a regular basis. One example
for that is for instance a sales report, showing revenue for a given sales
hierarchy.
Those regular reports might still continue and can also in the future be produced from
SAP BW / SAP Business Objects and they help significantly to control and manage the
business outcome on a day to day basis.
But if HANA is the database for BW, we can do some additional analytics on the granular data
which is stored in the Operational Data Store (ODS) which is also sometimes called
Data Store Object (DSO). Both are just flat Database tables or sets of database tables
containing millions, sometimes billions of records.
My use case is to use the predictive functions in HANA and the data mining capabilities to explore
this granular data further to find new sources of revenue or significant cost
reduction opportunities. We are looking basically for things we don’t know
today or the “unknown”.
For example:
What are customers in a certain segment doing with us that allows us to
conclude or predict a certain customer behaviour?
To make it clear what type of queries people would be looking at
(these are special examples of queries that typically do not come out of the box from BW
or other data warehouses and would require specific report development.)
Here is a (non exhaustive list) list of potential query outcomes with subsequent potential
business changes and value impact:
Potential
Query Results | Subsequent Business
Change | Value
Impact |
|---|
Customers that bought product A
bought in 65% of cases product B | Product display change, sales memo to all sales people to focus on cross sell into
product B | Revenue
Increase |
Customers that bought product A with credit card came in 55% of the cases within 3 weeks
back into the store to buy product B with cash | Register customer when buying product A and notify on product B 1 week after purchase | Revenue
Increase |
Transport vendor A has achieved only 18% DIFOT (Delivery in Full On Time) where he accepted the tender late | Apply new rule for tender process to avoid selection of transport vendor A after late acceptance of the tender | Cost avoidance, Quality improvement, Less customer complaints, less credit notes |
In 89% of the cases where a severity A risk rating was applied to incidents it happened when the incident was created | Introduce a behaviour change where incidents are created first,
rated and then checked by a Sustainability Manager to ensure the rating is correct | Reduction of costs of Compliance, Reduction of legal fees and penalties |
The total cash balance in all cash registers in all outlets is maxed at 6pm and has a second peak at 8:30pm | Add an additional cash collection at 6pm and 8:30 pm | Positive Cash flow impact, Robbery risk reduction |
In 89% of the cases the CFO had to borrow money on the financial market to pay the employees just 3 days before he received payments from customers | Apply SAP Financial Supply Chain Management,
synchronise customer payments targets to payout forecasts | Improved Cash Flow, Reduced DSO (Days Sales outstanding) |
Thecustomer usage pattern of electricity shows that most of his daily consumption
is being done between 18:00 and 19:00 | Provide a special discount for the electricity usage during this time to gain competitive advantage | Attract new customers, increase sales |
Dependant on the type of industry or business this list can
be amended endless. Variations of these questions can be a significant worklist
for a data analyst and becomes a painpoint, if the system response time becomes
too slow. SAP HANA removes this painpoint.
When analysts explore the data on the most granular level, they
typically have something in mind they are looking for. They might find it after
applying the first query, sometimes they might find it only after they put a
sequence of queries onto the dataset.
SAP HANA provides though its capability to explore high amounts of data
very fast and its ability to apply predictive functions to the explored data.
So for the first time in history we have now a tool (SAP HANA) available that
allows us to apply a reasonable speed to these investigations.
People might argue that other data warehouses have the same ability / capability
but I really believe that Data Analysts have to drink a lot of coffee with other data
warehouse solutions while they wait for the query results to return.
