Big Data: How can it enhance your strategy?

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1 7 Big Data: How can it enhance your strategy? Practice Area: IT Strategy Topic Area: Big Data Connecting the data dots for better strategic decisions Data is essential for organisations looking for answers to key strategic questions Traditional data analysis relies on structured sources which are useful for describing what has happened - not necessarily why it did or what will happen next Big Data the process of linking structured and unstructured data in cost-effective virtual data warehouses to reveal new patterns, trends and insights offers organisations increased confidence in predicting strategic outcomes Making a successful start on the journey is not difficult as shown by the varied examples highlighted in this paper. Paul Diviny, Timothy Morris, Phil Noble May 2013 Page 1

2 INFORMATION TECHNOLOGY STRATEGY Big Data: How can it enhance your strategy? Big data can help us to do what we are trying to achieve, but at a much lower cost of IT ownership A leading organisation wanted to get into the heads of customers, so it started a datacollection initiative to gain a deeper understanding of how it was interacting with customers. Now, years later, it faces the problem of having too much data, and is trying to process this data into useful information in a cost-effective way. Strategically, the organisation is trying to find a useful and cost-effective way of expanding its existing analytics tools with a $1 million investment in a big-data strategy. Expected returns on this investment are high and stem from a leap forward in analytic capabilities where they feel Big data can help us to do what we are trying to achieve, but at a much lower cost of IT ownership. Their Business Intelligence Division now sits in the customer department and has a stake in marketing the brand, customer experience and the products themselves. Is your organisation or business unit facing a similar scenario? Data is essential to the strategic planning process. With access to the right data, organisations can answer key questions driving their future strategies. Answering strategic questions requires sourcing and interpreting the right internal and external data. Similarly, during and after the strategy execution phase, using data for tracking implementation against performance measures becomes critical. Most industries operate in a world with vast quantities of data arriving at high speed in a myriad of forms. Finding data is not a problem for many large organisations. The real strategic question is what it all means. Exhibit 1 illustrates typical data driven strategic questions. EXHIBIT 1: USING DATA IN STRATEGIC CONSIDERATIONS Business planning Where are growth opportunities? What is emerging in our industry? Competitors Product development Process improvement Are our competitors satisfied with their current position? Where are they vulnerable? What do our customers think of our new idea? Is there likely to be sufficient demand? What processes drive the most complaints? What is the Voice of the Customer? Risk Management How can we detect and mitigate against risk? Segmentation What new segments are identified when we look at underlying customer attitudes and hidden behaviours? Customer Service What are the customers channel preferences and service needs? Where do we find data? Typically, data is structured into databases (inside Financial Systems, Enterprise Resource Planning, or Customer Relationship Management Systems) or sourced from external organisations such as the ABS and market research agencies. A key disadvantage of structured data sources is that they ignore a host of customer and competitor data that is unstructured in nature. Sources of unstructured data could include corporate , contact centre conversations ( , fax, call), web clicks, and social media interactions (Facebook, Twitter, YouTube, etc.) to name a few. Page 1

3 Problems with traditional approaches to collecting and analysing data 80% of data exists in unstructured form Data analysis is sometimes referred to as business intelligence. Business intelligence makes organisations more effective in: customer retention and acquisition; identifying process improvement opportunities; detecting fraud and other operating risks; and managing programs of work. Traditional business intelligence solutions focus on structured and repeatable analysis. The first step involves information requesters determining which questions to ask, such as: what are my customers doing with us or who is leaving us? The next step involves the Information Technology or Customer Analytics department who typically capture only what is needed - structuring the data to answer these business questions. This cycle can take weeks if not months if demand is strong (a common occurrence during strategic planning periods) or if a bottleneck in IT exists. In traditional business intelligence, decision makers look at historical trends and use these to make predictions often with low confidence. Newer forms of tailored business intelligence attempt to link history with current developments, such as what people are saying now that could explain their future behaviour. Big Data makes links to reveal new patterns, trends and insight Big Data solutions provide a platform for fast, iterative and exploratory analysis. With Big Data, the process is reversed, with IT delivering a platform to enable creative discovery. The business can then explore what questions could be asked. These might include questions on brand sentiment, product strategy, and fraud prevention. In Big Data (Exhibit 2), the platform stores, refines and analyses all data sources so that the business can explore data for questions worth answering. This cycle can take days, does not rely on specialist IT skills, and can evolve to near real time. The potential value of big data is a function of the number of relevant, disparate datasets that can be linked and analysed to reveal new patterns, trends and insights whilst preserving the privacy rights of individuals. EXHIBIT 2: BIG DATA EXPLAINED Input Volume Increasing volume of data, streaming data Velocity Increasing speed in which data is produced Variety Data in multiple formats from multiple sources (e.g. s, SMS, text document, PDFs, video, audio) Extract value Outcome Relevance Understanding links, relationships, and matching between data sets, tailor solutions Insight Managing information overload to identify what all the data means to the organisation Confidence Identifying new trends and understanding dynamically changing data, all in real time Big Data presents decision makers with compelling data to help increase prediction confidence. Since the data is more relevant, timely and insightful than other forms, decision making is far more outcome focused. Further, the IT investments required to house these insights is far less than expected as data is organised in virtual ways. Benefits of linking data sources for strategic planning With over 80% of relevant data existing in unstructured form, organisations limiting their frames of reference to internal or external structured data are not getting the full picture. Exhibit 3 identifies the benefits of bringing unstructured data into the decision making process. Structured data assists in answering what questions. However, why questions and deeper insights require organisations to also factor in unstructured data. Page 2

4 Structured Data Structure and Unstructured Data EXHIBIT 3: BENEFITS BIG DATA DELIVERS Transparency Make information more transparent and useable at a higher frequency, identify risks, and detect fraud Increasing confidence in predictions Timeliness Accuracy Tailoring Decision making New services Information at your fingertips, presented in real, or near real time Increase accuracy and detail of business insights, and improve cost analytics Achieve a more narrow customer segmentation to develop more tailored solutions, and understand customer behaviour Provide more sophisticated analytics to inform decision making, and quantify risks Identify trends, potential new service offerings, cross service opportunities, identify leads and market sentiment MORE REVENUE INCREASED EFFICIENCY IMPROVED RISK MANAGEMENT Exhibit 4 outlines how Big Data leverages existing business intelligence assets to improve strategic decision making confidence. The ability to combine unstructured and structured data sets helps organisations build on their investments in tailored business intelligence or take the leap from more traditional approaches. EXHIBIT 4: BIG DATA COMPLEMENTING TRADITIONAL BUSINESS INTELLIGENCE Big Data Tailored BI Traditional BI Descriptive (what happened) Alert (what s happening now) Predictive (what will happen next) Exhibit 5 demonstrates the value drivers for investing in Big Data and provides examples of specific business applications. A key Big Data value driver is a focus on revenue and profits via increasing retention, capturing new business, and improving marketing and related outcomes. A second key driver is improved efficiency through identifying process issues impacting operations and eliminating redundancy in reporting. Another key driver is reducing operating risks such as fraud and safety issues. The investment costs of capturing these benefits include the technology itself, recruitment of data scientists and, potentially, a minor structure reorganisation. Page 3

5 EXHIBIT 5: BIG DATA VALUE DRIVERS LEVERS POTENTIAL APPLICATIONS Increase retention Identify customers and staff expressing dissatisfaction and demonstrating potential attrition behaviours Make timely interventions to ensure retention Grow revenue and profits Capture opportunities Identify new market trends before your competition based on what your customers and prospects are saying Improve competitor intelligence by sourcing previously hard to obtain information on their strategies and tactics Many industry sectors are embracing Big Data Big data value drivers + Improve efficiency + Reduce risks Improve outcomes Solve process issues Eliminate redundancy Lift forecasting accuracy for new products and services Increase marketing conversion rates by micro targeting prospects Listen to the Voice of the Customer Identify process improvement opportunities before they become issues Reduce or eliminate unwanted and out of date reporting tools Eliminate IT overheads associated with producing information Detect potential internal and external fraud events Identify OH&S, product safety and other issues that could create operating risks Big Data in action Leading organisations in different industries are using Big Data to better control their business and identify opportunities. Exhibit 6 shows several examples of how Big Data is used for: Gleaning insight into customers; Increasing revenue; Improving risk management; Reducing costs. EXHIBIT 6: EXAMPLES OF BIG DATA APPLICATION Amazon & PayPal: detecting fraud to maintain service integrity Deutsche Postbank: improving credit risk monitoring for specific customers T-Mobile USA: halving customer defections via analyses of transactions, channel interactions, social media data, CRM and billing US Xpress: improving fleet management and reducing operating costs via analysis of fuel use, tyre condition and GPS IBM: predicting customer churn faster by analysing 500 million daily call records in real-time American Express: creating new services to enhance customer acquisition and retention programs Progressive Casualty Insurance: providing real-time analysis of driving habits in return for reduced premiums Honda: predicting new product success or failure via customer intelligence analytics ING Direct: monitoring customer experience to identify customer segments and churn behaviours Oxford Uni: gaining insights into student enrolments to assist with retention and recruitment Exhibit 7 provides an example of an equipment finance organisation. Big Data, in this format, could provide hot leads to its equipment sales and finance network, based on linking lease finance and dealer sales databases, to unstructured data provided by would-be customers of the dealer. Page 4

6 EXHIBIT 7: DASHBOARD BASED ON BIG DATA Customer opportunity Sourced from different databases Challenges can be overcome Strictly private and confidential Page 13 Relationship between dealers and customers A system and process that delivers great leads to Dealers, can enhance the dealer relationship and bring benefits to the Finance Company Getting started Experienced practitioners report the need to demonstrate short term revenue and cost saving opportunities via use of a prototype solution. This approach has several benefits including: Demonstrating tangible outcomes of Big Data on a specific business problem or issue Determining the future challenges in terms of data quality, skills in data science, and organisational design Convincing senior executive and Board stakeholders of its real value Identifying elements of a full business case required to support a full implementation. The example shown in Exhibit 7 is an example of a prototype solution. Big Data has challenges that can be overcome Implementing Big Data solutions is not without challenges which include solution maturity, organisation limitations, data security and privacy concerns, capability and critical thinking, and technology. Solution maturity: In Australia, Big Data solutions are mostly in pilot phase. Organisation limitations: Talented data scientists are not easy to find and an organisation s business intelligence (BI) culture needs to change to embrace the user-driven approach to analytics and how to complement existing BI investments. Privacy concerns: As more data is available, acceptable use of personal data may become an issue for an organisation s stakeholders. Data security is also important. Australia has relatively strong data privacy legislation and high public and media awareness of related issues. However, there is a school of thought that the public is willing to allow trusted organisations access to their behaviours and attitudes provided they see value. Capability and critical thinking: Providing increased access to data, especially in an unstructured form, can lead to misinterpretation and misuse of analysis by less skilled staff. Technology: Chosen technology solution must keep up with the increasing volume, velocity and variety of data. Ways to overcome these challenges include: 1. Investing in a Big Data Prototype solution analysing clickstreams, log files and Voice of Customer (VOC) contact centre records to deliver quick wins. 2. Leveraging service providers and not being afraid to use existing talent to fill Data Scientist roles. 3. Adopting a transparent social contract with stakeholders on data security and privacy issues. 4. Integrating existing IT infrastructure either loosely or deeply based on the business case. Page 5

7 Could Big Data suit your strategic needs? If your answer is yes, SPP can support your Big Data needs from start to finish. At SPP we start by asking the critical question first - what do you need to know and why? We support organisations to build upon their existing capabilities, develop approaches that enable strategic decision making and embed insights into business processes that are simple to understand and readily actionable to achieve the business goals. Exhibit 8 outlines key questions SPP believes organisations contemplating a data driven strategy need to answer. EXHIBIT 8: SPP SUPPORT FOR BIG DATA INITIATIVES Phase 1: Scope Phase 2: Validate Phase 3: Capabilities Phase 4: Proof of concept Phase 5: Invest Phase 6: Implement Key question What information needs will help us achieve our strategic objectives? What does the ideal future state for insightful information look like? What skills, technology, and practices need to be in place before we start? What would a proof-of-concept look like? What determines whether we green light investment? How do we ensure insights are being embedded into decision making? Examples Reduce costs Improve customer retention Test product ideas Organisational change Cross departmental process Data Scientist Infrastructure Viability testing Pilot project ROCE Business Case Training Change management Five things you could do now An immediate action that can put you in a stronger position is to develop a plan to answer the Big Data question. Leading organisations already have the following five tactics in play: 1. Information needs: fully aligned to strategic objectives 2. Future proof: data collection tailored to capture strategic insight 3. Gap analysis: understanding what you have and don t have 4. Proof of concept design: knowing what this looks like and even having one in place 5. Business case for investing: assessing the benefits and costs of a deep investment With a depth of knowledge and experience, SPP is in a strong position to provide services to companies with Big Data issues. If you would like to discuss this article further, please don t hesitate in contacting either Phillip Noble ( ), Ben Apted ( ) or Paul Diviny ( ). Page 6

8 ABOUT SPP Strategic Project Partners is a generalist, strategy consulting firm. We support General Managers on difficult strategic and operational challenges. Established in 2005, SPP has delivered successful outcomes for a broad range of commercial and Government sector clients. As a result we have strong relationships with many businesses, from Top 50 listed companies through to small enterprises. When we deliver our projects, whether it s a strategic study or the implementation of large-scale change, we focus on: Strong project management Clarity of outcome An obsessive focus on project benefits Robust, fact based analysis Simple communication Bringing experience to bear ABOUT THE AUTHORS Paul Diviny Senior Engagement Manager Paul is an experienced General Manager, Consultant & Entrepreneur, and has worked in a range of industries including banking, superannuation, consumer finance, government and not for profit organisations. Phil Noble Director & Founder Phil has a broad range of experience in general management consulting, as well as in line management. Phil started SPP with the aim of bringing good strategy and general management practice to businesses with a minimum of fuss, and maximum impact. Phil has worked in financial services, government, entertainment, IT&C, and across strategy, corporate development and major capital projects. Phil has also worked for firms such as the Boston Consulting Group in the past. Tim Morris Consultant Tim has had broad experience in transport, higher education, global not for profits, retail and government assignments. CONTACT US Contact Phillip Noble ( ), Ben Apted ( ) or Paul Diviny ( ) if you would like to discuss this article. Page 7

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