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IS BIG DATA DELIVERING BIG VALUE TO GERMANY? Enhance business value with data driven decision-making

VOLUME I

WIPRO DACH

INSIGHTS

German companies have yet to realize the full potential of big data for strategic decision-making. Although firms are well aware of the value that data can add, a lack of required skill sets prevents them from making full use of it. In this article we describe the main challenges and present a five-level maturity model for describing the analytical capabilities of future businesses. As technology firms look to gain a footing in physical businesses, traditional firms must fill all these gaps in their data analytics framework.

DERIVING BUSINESS VALUE FROM BIG DATA ANALYTICS

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Main ChallengesWe need to take a closer look at how big data is structured in order to isolate areas that need improvement. Broadly we can break it down into two layers:

a. Data layer

This layer is primarily concerned with gathering of data from internal and external sources. Internal data sources may include product lifecycle management (PLM) systems, customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, content management systems, etc. Although these sources are complete in their own right, firms must ensure that such data is consistent across different lines of business and geographies.

German firms also need to access and integrate external data sources into their decision systems. External data has become much more relevant in recent years due to the growth of intelligent data generated by mobile phones, sensors (e.g. in medical equipment, industrial machinery), RFID and so on. With such a wide array of sources available, it is essential for firms to identify relevant data, set up a comprehensive data gathering framework and integrate it into their decision systems and processes.

b. Analytics layer

This layer is responsible for processing of data based on the strategic priorities of a firm. A powerful analytics layer would have characteristics such as:

• Strategic alignment – Its outputs should directly feed into high level decision-making for the firm

• Completeness – It should utilize data from all possible sources, both internal and external

• Openness – There should be a proper rationale for applying any algorithm or statistical tool to the given dataset

• Evolution – The latest in hardware and software technologies as well as sophisticated algorithms should be used

The emergence of data analytics has fundamentally altered the way we approach business problems. Decisions have become increasingly more data driven rather than based on gut feeling or individual experiences. This has become possible largely due to better data collection, storage and processing, and analytical technologies. The firms that are able to derive strategic insights from data (both internal and external) are the ones that have emerged as leaders in their industries.

The traditional paradigm of business intelligence (BI) largely relied on internal data sources (such as ERP Systems and CRM Systems) to support decision-making. Today, firms have a glut of data sources at their disposal — external data delivery firms have mushroomed; machines and sensors have become continuous data sources; mobility and social media have provided entirely new data streams. Despite such rapid progress, most German companies have not kept up with the pace of technological developments. The use of data for business decisions has remained somewhat tentative. The way forward is to nurture talent and alter the existing mindsets to encourage data-driven thinking.

Essentially, big data is about getting access to new data sources and using analytical methods to enhance existing products and processes. Most German companies continue to use traditional reporting and planning systems even today. Sophisticated statistical methods are still new to most companies and talent is scarce. Thus, German firms need to evolve from a traditional BI perspective to a closed loop of real time data gathering and advanced analytics.

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Traditional BI methods have been based largely on structured data from internal sources. This means that they are not suitable to utilize unstructured data like documents, emails and phone calls. While German companies value such data, they do not have the expertise to access it. Current responsibilities of IT departments do not cover this even though business divisions can benefit greatly. Hence, in order to leverage such unstructured data, firms not only need to hire qualified professionals, but also require proper ownership and accountability structures.

Further, incorporating external or unstructured data into the analytics layer can be a non-trivial task. Such data is generally available in diverse formats and needs to be normalized before it can be used. Further, care should be taken to avoid the text/

data mining bias with large datasets, i.e., ‘overanalyzing’ the available data to derive spurious insights.

Between these two layers, there is more value to be derived from improvements in analytics. The mandate given to IT departments has predominantly favored enhancements in the data layer. This can be achieved by using new sources as well as better structuring methods (voice/handwriting recognition, video content analysis, etc.) and storage technologies. On the other hand, for the business divisions, structuring and storing data becomes meaningless if useful insights cannot be derived from it. Analysis becomes even more cumbersome as different types of data are added to the mix. Indeed, the bottleneck for several German firms today is that they lack capabilities in the analytics layer.

Maturity LevelsBased on our discussion so far, we can outline a BI/Big Data maturity model for German firms depending on the extent to which they are leveraging big data technologies.

Management of comprehensive value-creation networks

Sector transformation and new business models

Internal and external processes

Internal processes

Business areas

Degree of capacity expansion

Degree of change in the classical value chain

Value creation

Support

Value creation

Support

Information = Strategic production

factor

Information = Dominant production

factor

Information = Strategic production

factor

Information intensity in performance

Low

High

Information intensity in the value chain

Low High

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1. Department Oriented Analytics

This level corresponds to the traditional model of business intelligence involving department specific data-gathering techniques. The disadvantage of such an approach is that firms are unable to leverage synergies between departments. For instance, the production department of a car manufacturer would have its own set of historical data related to product development life cycles. The sales and marketing department of the same firm would have data related to customer preferences, sales strategies, etc. In a department-oriented approach, each department would use its own data to enhance its functions and offerings. But clearly, if production data can be used in conjunction with insights from sales, the firm will be able to make cars that are better matched to customer requirements.

There may be several reasons why such collaborations do not materialize:

• The two data sources may have incompatible formats

• Departments may be located in different parts of the world and/or use different languages in their daily functioning

• Adequate communication channels may not be present

• Stakeholders within the firm may be unaware of the very possibility of data sharing

All such issues can be resolved if top management can take the initiative to look for such opportunities.

2. Process Oriented Analytics

In a firm working at the second maturity level, data analysis techniques are applied at the level of internal processes level. Such processes can be broadly divided into two types: core processes and support processes. Many of the German industries today are well versed with data analysis techniques for the latter but not the former. For instance, it is quite possible that human resources and finance functions of a car manufacturer have thorough data-management techniques in place while production and logistics do not. This may be

because commercial analytics solutions (like ERP Systems) for support processes are readily available in the market. On the other hand, the core processes are likely to be managed entirely by internal teams that do not possess the required expertise even though they may be aware of the potential of data analytics. Today, third-party systems for core processes (like PLM Systems) have also emerged, but it is still up to the internal teams to recognize the value of data management and implement these solutions.

3. Integrated Data Management

As the name suggests, this level involves integrating the data analytics function of both support and core processes — encompassing both internal and external processes. All internal and external data is brought together for enhancing cooperation across different functions. It is only at this level that a firm can utilize every data resource available to it. Let us take the example of the car manufacturer forward. It can use HR data to optimize worker productivity; PLM and marketing data can be combined to design a cost-effective yet customer-focused product; and, finally, external economic data can be used to determine the demand in each region and hence decide on pricing and supply. A firm looking to stay competitive in an evolving landscape must build such a unified data infrastructure. It should also set up a dedicated analytics team to communicate with IT and business teams and derive strategic insights.

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4. New Data-Driven Business Models

Firms in the first three levels of maturity largely use data analytics to augment their existing businesses. Thus, the next logical step up the ladder would be to adopt entirely new business models that leverage big data. For instance, a GPS device installed in a car can allow the manufacturer to track its mileage and hence the wear and tear. This would help in setting up a predictive maintenance service offering. Similarly, other sensors could track engine condition, driving behavior, etc., so that the production process could be upgraded to match customer requirements.

5. Data Ecosystems

This is one step ahead of data-driven business models and has not been reached by traditional German companies so far. It essentially involves establishing a data infrastructure that gets continuous inputs from devices being used. Analytics would mature from being a small factor in strategic decision-making to the primary driver. Examples for such a structure are today’s technology giants like Google, with its search engine, and Apple, with iTunes and App Store, which run data intensive businesses based on real-time analytics.

Today, German companies generally stand at the first two levels of big data maturity, with a few of them having reached the third

level of integrated data management. It is up to them to adopt rigorous data practices in their day-to-day functions in order to keep pace with the market. The top management should ensure that there is no gap between businesses and analytics. IT teams should find ways to bring device data into the fold of analytics. They should work with business teams to define the kind of analytics required and also specify the important KPIs.

The Way ForwardRecent developments in data analytics have blurred the lines between different businesses. Automobile firms like Daimler, BMW or Audi can greatly benefit from the same data analytics systems as, say, a supermarket chain like Lidl. Insights about customer behavior, regional preferences, weather conditions, etc., can help build the perfect car and also boost the profitability of farm produce. In such a scenario, manufacturing firms are likely to face challenges from technology firms like Google and Facebook that have a firm hold on analytics. Already, Google has attempted to venture into the automotive space with its driverless car project.

German firms today face a number of hindrances in setting up a comprehensive data ecosystem. For instance, manufacturing firms tend to deal with a number of suppliers — as much as 70% of the parts for a car maker may come from its vendors. Such firms may not have the power to embed a real time data infrastructure within their final products. A service sector firm like a telecom operator may have access to more data-oriented skill sets. However, there might be legal constraints preventing it from gathering and utilizing customer data. In all such cases, firms can collaborate with third parties and data experts to find alternative solutions. A car maker can include devices like the GPS, diagnostic equipment that tracks data from the engine microcontroller, etc., in their product offerings. A telecom firm can collaborate with online messenger services, search providers, etc., to better understand their customers.

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Author: Prof. Dr. Andreas SeufertProf. Dr. Andreas Seufert teaches Business Administration and Information Management in the fields of Management and Controlling at the University of Applied Sciences Ludwigshafen am Rhein. He is the director of the Business Intelligence Institute at the Steinbeis University in Berlin as well as the head of the working group “Business Intelligence” of the International Controller Association (ICV). Furthermore, he works as an expert consultant for renowned magazines and conferences.

Prof. Dr. Seufert has long-standing experience in the area of academic research and teaching. Among others, he has worked for St. Gallen University and the Massachusetts Institute of Technology (MIT). As a (co-) author and publisher of books, magazines and conference contributions, he has written more than 100 publications. He also has extensive international experience in IT and Management Consulting. The main areas of his research and consulting activities are Strategic Management, Information Management, Controlling and Corporate Management, Business Intelligence/Big Data and Corporate Performance Management, as well as Knowledge Management and Web 2.0.

Foradditionalinformation,pleasevisit:http://www.i-bi.de/

Conclusion

A key problem with the use of big data in German firms is at the organizational level. Business leaders must be able to understand the value of data to their companies. If they do not have an integrated data strategy, they need to set up ownership and roadmaps for it. The debate about who owns the business intelligence/Big Data function — whether it is IT or core businesses — needs to be settled. In traditional firms, since departments and processes exist as individual islands, any steps towards integration would have a big impact on competitiveness. It is important to note that before these firms can hope to benefit from data analysis, they must fill any gaps in skills and technologies.

With technology firms using their knowledge base to enter into physical businesses, the market is set to become more competitive. However, to a firm that can rise to the challenge, big data can present radical new opportunities.

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THE POWER OF ANALYTICSA new study by the Economist Intelligence Unit, commissioned by Wipro, finds a strong

relationship between earnings growth and strategic use of data.

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BIG VALUE IN BIG DATA

You would imagine there would be delight at the flood of numbers we are witnessing. That’s a flawed notion. It is ironic that in a world of shrinking resources, the one plentiful thing we have - data - is threatening to smother many.

Quality, not quantity

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In my interactions with global businesses, I get the feeling that IT folks are being overwhelmed by the stream of data they are now being subjected to. They don’t know where to look for value in Big Data. What they are saying is, “Can you turn quantity into quality?” What they mean is, “Help me find business use for this vast mountain of data we capture”.

The key to effectively leverage data is to first create a strong business case: How much money can it save me? How much time can it save me? What is the value of that time? How much can I cut back on resources if I use Big Data and analytics? What insights can it lead to that create additional business, result in new products, help address new markets, or ensure we stay ahead of competition? Can it help create customer delight? What is that customer delight worth? The odd thing is that many businesses think they need to tap fresh or additional data sources in order to unlock that value. Admittedly, this is often the case. But equally often we have seen that reality can be surprisingly - and pleasantly - simple.

Instead of more data, what could be missing are factors such as the availability of critical data in real time. Take the case of a manufacturer of heavy mining equipment. The manufacturer wanted to charge for equipment on per mile basis against stringent SLAs. A few years ago, that would have been a simple service to devise; it would have more or less meant that the manufacturer divide the cost of the equipment and maintenance by the expected average lifetime of the equipment. Using this method, admittedly over-simplified here, the manufacturer would have had to bring a vast amount of backup equipment to ensure SLAs were met. This would substantially increase the cost of the service, making it unviable. Not anymore.

The manufacturer is our customer. We enabled this service for the customer by monitoring assets in real time, predicting failure and ensuring the equipment is serviced before it breaks down-meeting demanding SLAs.

Leveraging data and analytics in real time has helped them create an alternative revenue stream. The fact that they can meet those stringent SLAs also means that their customers are happy.

In this instance, the quantity of data did go up substantially. But that was not adequate in itself. We had to sift through the entire data chain in real time: from capture, augmentation, cleansing and analysis to action. We were not trying to bridge a gap in understanding what was happening to the drilling equipment in the field. The data on equipment performance was always there for the asking. It could have been accessed as a historic data dump anytime. Instead, we had to turn to real time management of the data as well as the analytics.

Use cases for Big Data can be numerous. In each instance, organizations must first agree on the business value that can be mined from data. Only then should they embark on how to explore data to extract that value.

Turning Big Data into a mine of actionable intelligence requires an adept team. It needs appliances to handle and massage the data. In-memory computing, pattern hubs and the ability to harness data in motion are the new weapons to tame the Big Data problem.

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This is also where uncertainty begins to haunt IT teams: The resources required, the tool kit that will help sift through the chaos of numbers, the people, the pitfalls and the costs involved are not clear currently. Leveraging Big Data and analytics in your business is inevitable. Build a use case for it. Everything else will begin to fall in place. A strong use case is usually a good starting point because it lets you look for quality rather than get lost in quantity.

Improving customer insights

Analytics can help provide a 720 degree view of the customer, driving extreme personalization; offer competitive pricing to select groups of customers; improve brand loyalty; and drive consumption through targeted offers and campaigns

Driving machine data insights

Leveraging machine and device data can help prevent failures and save precious dollars from reduced downtimes

Size of digital universe 2011

1.8 zettabytes

Predicted growth 2009-2020

35 zettabytes/yr

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Preventing fraud

Fraud prevention by correlating data from multiple sources like financial transactions CDRs and more

Reducing cost of IT

Segregrating data, storage based on the type and importance of data; Leveraging low cost databases, Big Data platforms/Open Source platforms

“K. R. Sanjiv is the Senior Vice President and Chief Technology Officer for Wipro’s Global IT Business. As a CTO he is responsible for establishing the company’s technical vision and strategy and also leading various aspects of the company’s future technology development. It includes incubation of the emerging technologies, creating an ecosystem for the innovations in these spaces, IP management and creating industry and academic alliances.

Prior to this role, he was Global Head of Analytics & Information Management business unit. He carried P&L responsibility, strategy and operations of this unit globally reporting to CEO.

Sanjiv has over 25 years of enterprise IT experience, including consulting, application and technology development spanning multiple industry segments and diverse technology areas.

Author: K. R. Sanjiv

Since joining Wipro in 1989, Sanjiv has been involved in defining enterprise architectures for organizations that included technical models, transformation program definitions and governance models.

Sanjiv has spoken at leading CxO summits, industry and academic conferences on varied topics related to Business Technology. He holds a bachelor’s degree from Birla Institute of Technology and Science, Pilani.”

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IND/PMCS/WIPRO/JUN 2014 - AUG 2014

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