Data-driven DNA is about having the right toolset, mindset, skillset and dataset to evolve a major brand and seize today’s omni-channel opportunities. Whether it’s retooling and retraining for the multiscreen attention economy, or introducing digital innovations that transform both retail and healthcare, P&G is bringing data into every part of its core strategies to fight for the customer.
Striving for market leadership in consumer products is a non-stop managerial quest. In the struggle for survival, the fittest win out at the expense of their rivals because they succeed in adapting themselves best to their environment.
CMOs and CIOs everywhere agree that analytics is essential to sales & marketing and that its primary purpose is to gain access to customer insight and intelligence along the market funnel – awareness, consideration, preference, purchase and loyalty.
In this posting we illustrate a best-in-class “run-the-business” with Data/Analytics Case Study at P&G. The case study demonstrates four key characteristics of data market leaders:
- A shared belief that data is a core asset that can be used to enhance operations, customer service, marketing and strategy
- More effective leverage of more data – corporate, product, channel, and customer – for faster results
Technology is only a tool, it is not the answer..!
- Support for analytics by senior managers who embrace new ideas and are willing to shift power and resources to those who make data-driven decisions
This case study of a novel construct called Business Cockpit (also called LaunchTower in the Biotech and Pharmaceutical Industry) illustrates the way Business Analytics is becoming more central in retail and CPG decision making.
Here is a quick summary of P&G Analytics program:
- Primary focus on improving management decisions at scale – did the analysis to identify time gap between information and application to decision making
- “Information and Decision Solutions” (IT) embeds over 300 analysts in leadership teams
- Over 50 “Business Suites” for executive information viewing and decision-making
- “Decision cockpits” on 50,000 desktops
- 35% of marketing budget on digital
- Real-time social media sentiment analysis for “Consumer Pulse”
- Focused on how to best apply and visualize information instead of discussion/debate about validity of data
Who doesn’t want to achieve faster “time-to-information” and shorter “time-to-decision” for executives and managers with mobile BI? Who doesn’t want to disseminate insights or KPIs to front-line employees, such as field sales representatives, line of business managers, and field service employees?
The question is not whether Mobile BI is a good idea but how to execute this program in a low-cost way? How to design and deploy eye-popping “wow” apps? How to support, maintain and enhance these apps which are constantly changing? What technology and infrastructure to put in for a national or global deployment? Who is going to fund all this plumbing – corporate, LoB or IT?
Business Analytics solutions for “always-on” 3/4G enabled mobile devices – iPads, iPhones, tablets, smart phones – are becoming prevalent as the form factor becomes appropriate for BI. We are increasingly seeing firms build state-of-the-art dashboard solutions for iPads. The “post-desktop” apps provide senior management with intuitive interactive access to the company’s most important business KPIs and dealing with data overload.
Tablets, 4G Wireless and next gen displays (+gesture based, verbal interfaces) have enabled new productivity improvements and better ways to consume information, perform ad-hoc querying and scenario planning. Dashboard, heatmaps and scorecards on the iPad, iPhones and Androids are intuitive, attractive, powerful, available at any time and any place: a perfect mix for top managers, sales teams and even customers.
BI (and Information Management) is a natural fit for mobile devices. Managers, blue and white workers spend a majority of their time away from their desks. Most are traveling, walking about or driving from site to site. And it’s these mobile workers who need the most up-to-date information. They need mobile BI to retrieve data to make on-the-spot decisions, monitor operational processes and review KPI, and work-in-process dashboards.
Apple with its iCloud offering is attacking the consumer facing digital content big data problem. Big Data is challenging on many fronts from the insights (e.g., analytics and query optimization), to the practical (e.g., horizontal scaling), to the mundane (e.g., backup and recovery).
On June 6th, 2011 Apple Inc. launched its new purpose built digital locker service called iCloud for its 225 million iTunes accounts that frees the end-user from the tyranny of the device. The iCloud service is a cloud offering that would allow users to store digital files such as photos, MP3 music, videos and documents in the cloud and access them from Internet-connected devices like iPhones, iPads, iPods, iMacs and others.
So, what’s the big deal? They are addressing a classic BI data management problem: How to free up data trapped in “device and application jails” in a user-friendly way. The “scan and match” concept is quite applicable to large scale Enterprise Datawarehouses which suffer from data integrity issues as edge data capture and consumption devices proliferate.
Data ingestion, governance and management is a huge problem facing large organizations. As data volumes double every year, not having a basic data management strategy will become an Achilles heel. Most organizations unfortunately don’t know what data assets they have, where these assets are, how they are organized and how well they are secured. Apple shows a neat way to address the Big Data problem in personal cloud management.
However, it took until 1980s when decision support systems (DSS) became popular and mid 1990s for BI started to emerge as an umbrella term to cover software-enabled innovations in performance management, planning, reporting, querying, analytics, online analytical processing, integration with operational systems, predictive analytics and related areas.
Gartner 2014 magic quadrant shows the key players in the BI market. The different players are differentiated based on five abilities— ability to handle large volumes of data, ability to deal with data velocity, variety (structured and unstructured), visualization capabilities and domain/vertical specific accelerators.
Analytics is becoming three different markets. First of all, there is the BI market which is actually going through quite a bit of change itself. This is a more consolidated market than we have seen in the past and there is a tremendous amount of work being done by Oracle, SAP, IBM and others to kind of retool it for the next generation of BI. So it is a growing market, lots of upgrade, replatform, modernization demand, lots of clients who are finally realizing that the tools (visualization etc.) are ready to give them some of the capability that they have historically cared about.
The second part of the market is what is called Advanced Analytics. Here you need PhD level data scientists who have backgrounds in machine learning, industry specific domain modeling, and different types of data science who can apply that in a very specific way to specific industry problems. This is a rapidly growing part of IT Services. Also, there are just not enough data scientists to go around.
The third part of the market is Analytics as a Service. This is about leveraging software-as-a-service platforms as opposed to on-premise. This is about a business model that is more like Business Process Outsourcing (BPO). Clients buy business outcomes; they don’t buy transactions and FTEs.
The analytics market has thousands of boutique consultants who are specialists in particular industries or specific technologies. It includes all the major technology providers, who are all trying to advance their business and capabilities that they are bringing to the market. And then there are vendors who are just bringing sheer capacity of data science skills to the market and they are coming in from a completely different angle of basically just renting the expertise of their data scientists into the market.
The market is incredibly fragmented. We are in the early stages of growth in the market. Every single one of our clients is building this capability internally and they are looking for more services from vendors, because the opportunity to apply analytics is in every single one function whether it is a customer analytics, industrial Internet, e-commerce platform, is growing. Analytics is embedded into literally every single business interaction.
BI, Analytics [and Big Data] Market Sizing
More recently to support a new generation of cost cutting and growth initiatives, corporations are investing heavily to gain near real-time actionable insights (historical and predictive), and from a mix of disparate spreadsheets and myriad of systems (legacy, internal silos, customer facing, suppliers, partners, etc.).