In part one of this two-part series – A year on: The promise of SAP HANA for Big Data analytics (Part One) – we outlined Yellowfin’s decision to add support for HANA in the latest release of its Business Intelligence (BI) software –
In part one of this two-part series – A year on: The promise of SAP HANA for Big Data analytics (Part One) – we outlined Yellowfin’s decision to add support for HANA in the latest release of its Business Intelligence (BI) software – Yellowfin 6.1. We now dig deeper into the benefits.
The ability for companies to capitalize on their information assets remains one of the highest priorities for organizations. Yet, delivering on that aim poses many challenges; especially when the organization has very large datasets.
In-memory database computing is a disruptive change that provides the speed to power analytics at unprecedented performance levels, while remaining cost-effective. The growing interest in SAP HANA’s capabilities is linked directly to increasing organizational data volumes and demand for faster actionable information.
Who can benefit from HANA? Let’s look at Retail as an example
It’s clear that mushrooming data growth, coupled with the decline in relative technology costs capable of managing and leveraging that information, has led many organizations to initiate (or consider initiating) Big Data analytics programs.
SAP has stated that customers have realized gains as high as 100,000x in improved query performance when compared to disk-based database systems. HANA also manages to persist that data on disk, making it suitable for analytical applications and transactional applications.
Large distributed retail networks generate huge amounts of rich data over time – sales, tender, money movements and inventory are the raw fuel for analytical analysis. The challenge is processing that level of information as close to real-time as possible for the benefit of the business.
While it’s true you can do BI with old data, there are many opportunities to be exploited by the business having immediate data knowledge; for example:
- Track the effect of marketing initiatives close to real-time
- Perform A/B testing of online channel promotions and tweak based on customers’ real-time reaction
- Discover a spike in a products’ demand and quickly react to ensure adequate supplies are available
- Area and regional managers can track the performance of stores and departments with constant updates
- Cash management and collection at counter and store level are visible in real-time and can be managed accordingly
- Excess stock can be discounted in stores, or online, with the price-point altered to enable the best clearance in the shortest time
- Inventory levels and margins are tracked much faster, enabling profit maximizing decisions
- Fraud detection, especially around the use of credit cards, can be more reactive – security staff can contact the counter staff within minutes. And, even if the person of interest has vacated the premises, a fresh description of the individual can be generated
Wanting these insights is one thing, but organizations must be able to:
- Convert data into actionable analytics quickly
- Collaborate faster to make decisions
- Have an organizational attitude to act quickly when the data demands it
Technology can solve the first two
- SAP HANA provides retailers with real-time access to critical information and allows for nearly real-time interactive analysis not possible with traditional database technology. SAP HANA solves the dual problem of processing huge datasets, and speed-of-access.
- Yellowfin’s connectivity support in its 6.1 release gives retail managers and executives the ability to leverage the data speed of HANA with intuitive reporting tools, rich dashboards and the industry’s leading Collaborative BI and Mobile BI capabilities.
And the third …
However, the technological components underpinning a Big Data analytics program aren’t enough to ensure BI success. Culture change – establishing firm strategies and IT-business alignment – is critical to the success of reporting and analytics initiatives. This cultural change requires organizations to transition from slow ’gut feel’ actions to agile data-based decision-making, allowing them to discover opportunities faster and take accurate fact-based action to bring more dollars in the door. To achieve this, IT will need to be more aligned to the revenue goals of the business, and to commit to the changes necessary to fuel the business with the real-time insights it needs to make competitive and successful decisions.
