Big Data Business Value Creation- Valutrics

How does one survive in a world where competitive advantage via analytics can be so short-lived? By constantly innovating, thinking differently, and looking at new sources of data and analytic tools to bring to light those significant, material, and actionable insights that can differentiate your business from that of your competitors.

Big data has changed how the terms such as valuable, important, and successful are quantified. This fuels the insights that are the source of competitive advantage and business differentiation. New big data sources, plus new advanced analytic capabilities, enable higher fidelity answers to these questions, and provide a more complete understanding of your customers, products, and operations that can drive business impact across various business functions, such as:
• Merchandising to identify which marketing promotions and campaigns are the most effective in driving store or site traffic and sales.
• Marketing to optimize prices for perishable goods such as groceries, airline seats, and fashion merchandise.
• Sales to optimize the allocation of scarce sales resources against the best sales opportunities and most important or highest potential accounts.
• Procurement to identify which suppliers are most cost-effective in delivering high-quality products in a predictable and timely manner.
• Manufacturing to flag machine performance and process variances that might be indicators of manufacturing, processing, or quality problems.
• Human Resources to identify the characteristics and behaviors of your most successful and effective employees.

Data Monetization Opportunities
Data monetization is the center issue of the big data discussion: How do I leverage my vast wealth of customer, product, and operational insights to provide new revenue-generating products and services, enhance product performance and the product experience, and create a more compelling and “sticky” customer relationship?

Digital media companies like Yahoo!, Google, Facebook, and Twitter have worked to master the data monetization process. They must because their entire business model is built on monetizing data. These companies work with bytes to create services, unlike most other companies who work with atoms to build physical products like shoes, tractors, houses, and burrito bowls with double chicken and guacamole.
So what process do these digital media companies go through to identify how to monetize their data assets? The data monetization process starts with two key understandings:
1. Who are my target customers (targeted personas) and what business solutions do they need for which they are willing to pay?
2. What data assets do I have (or could I have)?
Once you have a solid understanding of these two questions, then you are in a position to start the data monetization process.

Understanding the Target Users of Digital Data Assets
First, digital media companies need to identify and understand their target customers: who is making the big marketing and campaign decisions, and what information and insights do they need to make those decisions? Digital media companies target the following three customers or personas: Media Planners and Buyers, Campaign Managers, and Digital Media Executives. These digital media decision-makers buy the following “solutions”:
• Audiences, such as soccer moms, country squires, gray power, and weekend warriors
• Inventory (like sports, finance, news, and entertainment) available on certain days and times of days
• Results or measures, such as Cost per Thousands (CPM) of impressions, Cost Per Acquisition (CPA), product sales, or conversions (where conversions could include getting a visitor to share their e-mail address, request a quote, or schedule a reservation)

For each of these targeted personas, the digital media company needs to understand what questions they are trying to answer, what decisions they are trying to make, under what circumstances they are making these decisions, and within what sort of environment or user experience they are typically working when they have to answer their questions and make their decisions.
Next, digital media companies assess the breadth, depth, and quality of their data assets, including:
• Visitors and their associated demographic, psycho-demographic, and behavioral insights
• Properties and the type of content and advertising real estate (e.g., full banner, pop-under, skyscraper, leaderboard, half-page) that is provided on properties (like Yahoo! Finance, Yahoo! Sports, or Yahoo! Entertainment)
• Activities that visitors perform on those properties (for example, they viewed a display impression, moused over a display ad, clicked a display ad, entered a keyword search) including how often, how recent, and in what sequence
This data assessment process should also include what additional data could be captured through data acquisition, as well as through more robust instrumentation and experimentation techniques.

Data Monetization Enrichments
The key challenge is then to transform, augment, enrich, and repackage the data assets into the solutions that the target digital media customers want to buy. For example, digital media companies instrument or set up their sites and tag their visitors (via cookies) to capture visitors’ web site and search activities in order to determine or ascertain additional visitor insights, including:
• Geographic information such as ZIP code, city, state, and country
• Demographic information such as gender, age, income, social class, religion, race, and family lifecycle
• Psycho-demographic information such as lifestyle, personality, and values
• Behavioral attributes such as consumption behaviors, lifestyles, patterns of buying and using, patterns of spending money and time, and similar factors
• Product categories of interest (Schmarzo likes Chipotle, Starbucks, the Cubs and the Giants, and all things basketball)
• Social influences such as interests, passions, associations, and affiliations.

With this information in hand, the digital media company needs the data processing capacity and advanced analytical skills to profile, segment, and package those visitors into the audiences that advertisers and advertising agencies want to buy.
This data transformation, augmentation, and enrichment process is then repeated in converting properties into inventory, visitor activities into digital treatments, and campaigns into results such as sales and conversions.

Based on this digital media example, you need to go through the following steps in order to better understand how to monetize your data assets.
1. Identify your target customers and their desired solutions (solution capabilities and required insights) in order to optimize their performance and simplify their jobs. Identify and profile the target business customers or personas for those solutions, and internalize how those customers will use that solution within their existing work environment. Quantify the business value of those solutions, and document the business questions the users need to answer and business decisions the business users need to make as part of the desired solution.
2. Inventory and assess your data assets; that is, identify the most important and valuable “nouns” of your business. Understand what additional data could be gathered to enrich your data asset base via data acquisition and a more robust instrumentation and experimentation strategy.
3. Understand the aggregation, transformation, cleansing, alignment, data enrichment, and analytic processes necessary to transform your data assets into business solutions. Document what insights and analytics you can package that meets your customers’ needs for a solution that optimizes business performance and simplifies their jobs. Identify the data enrichment and analytic processes necessary to transform data into actionable insights and understand how those insights manifest themselves within the customers’ user experience.

There are numerous opportunities for organizations to improve product performance, enhance product design and development, preempt product failure, and enhance the overall user (shopper, driver, patient, subscriber, member) experience. More and more, the data and the resulting insights teased out of the data will become a key component, and potentially a differentiator, in the products and services that companies provide.

Big Data Value Creation Drivers
The key to the big data envisioning and value creation process is to understand the “big data business drivers.” There are four big data business drivers that can be applied to an organization’s key business initiatives or business processes to provide new insights about the business (across customers, products, operations, markets, etc.) and improve decision-making.

1: Access to More Detailed Transactional Data
Access to more detailed, more granular, structured (transactional) data enables a higher degree of fidelity to the questions that the business users are trying to answer and decisions that the business users are trying to make. For example, what types of questions could I answer and what decisions could I make if I had the ability to access and analyze more detailed transactional data, such as point- of-sale (POS) transactions, call detail records, radio frequency identification (RFID), credit card transactions, stock transactions, insurance claims, and medical health readings?
Access to more detailed transactional data is probably the “lowest hanging fruit” for most organizations—to take advantage of the transactional data, sometimes called “dark” data, they already collect. Due to today’s technology limitations and data warehouse cost factors, most business users only have access to a limited amount of data that is supporting their operational and management reporting. However, big data technologies provide the ability to access and analyze all the detailed and granular transactional data. Access to all the detailed transactional data can fuel the creative juices of the business user to ask more insightful “what if” questions, such as:
• What’s the potential business value of supporting more localization by developing forecasts and product plans at the product category, store, and department levels?
• What’s the potential business value of supporting more seasonal decision-making by developing marketing plans at the customer segment, product, ZIP code, and season levels (such as Christmas, 4th of July, Valentine’s Day)?
• What’s the potential business value of triaging claims patterns at the policy type by rider, claim, week, or day levels?
• What’s the potential business value of making network capacity and utilization forecasts based at the ZIP+4, local events, and season levels?
the potential to analyze your existing transactional data across multiple dimensions of the business—dimensions like location (specific outlets, branches, or stores), product, day of week, time of day, holiday, customer behavioral category, customer demographic category, and others—and at a lower level of granularity can dramatically improve your organization’s ability to uncover actionable and material business opportunities.

2: Access to Unstructured Data
The ability to integrate the growing volumes of unstructured data with your existing detailed structured transactional data has the potential to radically transform the types of insights that can be teased out of the data. The unstructured data can provide new metrics and dimensions that can be used by the business stakeholders to uncover new insights about your customers, products, operations, and markets. For example, what is the potential business impact of having access to internal unstructured data (such as consumer comments, e-mails, physician notes, claims explanations) as well as external unstructured data (such as, social media, mobile, machine, or sensor generated)? The business users could leverage the new metrics, dimensions, and dimensional attributes gleaned from unstructured data sources, coupled with the detailed transactional data, for finer-fidelity, more complete analysis and decisions to answer questions such as:
• What’s the business potential of leveraging new insights about my customers’ interests, passions, associations, and affiliations (gleaned from social media activities) into my customer acquisition, maturation, and retention business processes?
• What’s the business potential of adding sensor-generated performance data into your manufacturing, supply chain, and product predictive maintenance models?
• What’s the business potential of integrating third-party unstructured data sources (local weather, economic news, local events) into hospital or doctor office staffing and patient care decisions?
• What’s the business potential of integrating social media data into your claims fraud analysis to identify unusual claim submissions among communities of linked individuals?

3: Access to Low-latency (Real-time) Data
The ability to provide real-time (or low-latency) access to data is a game changer that can enable new monetization opportunities. The biggest problem with today’s batch-centric data platforms is that many customer and market opportunities are fleeting—they appear and disappear before one has a chance to identify and act on them. Think, for example, of the business potential of location-based services to communicate to your customers in real-time as they are making their buying decisions. In other words, what is the business potential of having real-time or low-latency access to key data sources and business metrics in order to shrink the time between when a customer, product, or operational event occurs and when that data is available for analysis and decision-making? What is the business potential of being able to update your customer, product, risk, and operational analytic models on-demand, based on immediate business, market other external events events (such as your favorite baseball team winning the World Series, financial markets jumping 4 percent in a single day, or a destructive hurricane predicted within the next two days)?

Access to low-latency, real-time data can fuel the creative thought processes in the timeliness of the types of questions you could answer and decisions you could make, such as:
• What’s the business potential of making customer acquisition, predictive maintenance, or network optimization decisions on a minute, hourly, or “on-demand” basis?
• What’s the business potential of updating analytic models on-demand based on current market, economic, or local events (e.g., weather, traffic, concerts, professional football game)?
• What’s the business potential of continuously updating fraud detection models based upon unusual activities amongst a social group of users?
• What’s the business potential of updating hospital staffing and inventory requirements based on local health incidents or disease outbreaks?
• What’s the business potential of updating your distribution schedules and planned deliveries based on current weather, traffic conditions, and local entertainment or sporting events?

4: Integration of Predictive Analytics
The integration of predictive or advanced analytics into key business processes holds the potential to transform every question that business users are trying to answer, and every decision they are trying to make. This is really about introducing a whole new set of verbs to the business stakeholders—verbs such as predict, forecast, score, recommend, and optimize. These new verbs can help business users envision a whole new set of questions to ask around the potential business impact of predicting what could happen, recommending a specific course of action, or forecasting the impact of different decision scenarios.

Integrating predictive analytics into key business processes and getting business users to embrace these new verbs can produce more predictive answers to key questions, such as:
• What’s the business potential of leveraging predictive analytics to optimize network operations, marketing spending, and staffing decisions?
• What’s the business potential of leveraging predictive analytics to predict the financial impact of pricing, route, or supplier changes?
• What’s the business potential of leveraging predictive analytics to score customers for fraud, retention, up-sell, and likelihood to recommend (LTR)?
• What’s the impact of scoring patients for treatment response and readmission likelihood?
• What’s the business potential to score partners for quality, delivery, and service reliability?
• What’s the business potential of leveraging predictive analytics to forecast network loads (based on economic conditions and local events) or forecast the performance of new product introductions (based on consumer sentiment and product category dynamics)?
• What’s the business potential of leveraging predictive analytics to recommend next best offers to improve customer satisfaction, customer retention, or preventive patient treatment?

New Senior Management Roles
Organizations are starting to realize that they need to treat their data and analytics as strategic corporate assets. This is leading to the creation of two new senior management roles: the chief data officer and the chief analytics officer. These two new roles will be involved in proactively managing the company’s data assets and analytics intellectual property.

The chief data officer will be responsible for acquiring, storing, enriching, and leveraging the company’s data assets. This role is likely to be filled by people with an economics or finance background as they look at ways to put economic value on the data that they have and want to acquire.
The chief data officer role could cover the following responsibilities:
• Data inventory: Many organizations don’t even know what data sources they have, so this role would be responsible for inventorying data (looking for unnecessary and redundant data purchases) and determining how that data is being used (to determine if the organization should continue to capture the data). This role would also have the critical responsibility for identifying and placing value on external data sources that could be acquired.
• Data economic valuation: Establish a framework around which to determine the economic value of the organization’s data, especially as companies look to acquire more external, partner, and third-party data.
• Data monetization: Establish a process to continuously evaluate the organization’s data assets for monetization opportunities through improved decision-making, integrating data into physical products, or packaging data for sale to other organizations.
• Instrumentation: Develop strategies to determine how to use tags, beacons, and sensors across operational, web, and mobile platforms to capture additional customer, product, and operational data.
• Data governance: Develop and enforce (audit) a set of processes that ensures that important data assets are formally and consistently managed across the enterprise to ensure the appropriate level of data cleanliness and accuracy.

The chief analytics officer will be responsible for capturing and tracking the analytic models and resulting analytic insights that are developed and deployed throughout the organization. The ideal chief analytics officer probably has a law degree to legally protect the organization’s analytical intellectual property (IP) including—data models, analytic models, and analytic algorithms. The chief analytics officer role could cover the following responsibilities:
• Analytic assets: Collaborate with the data science team to inventory analytic models and algorithms throughout the organization.
• Analytics valuation: Establish a framework and process for determining the financial value of the organization’s analytic assets.
• Intellectual property management: Develop processes and manage a repository for the capture and sharing of organizational IP (check-in, check-out, versioning).
• Patent applications: Manage the patent application and tracking process for submitting patents to protect key organizational analytics IP.
• Intellectual property protection: Monitor industry analytics usage to identify potential IP violations, and then lead litigation efforts to stop or get licensing agreements for IP violations.
• Intellectual property monetization: Actively look for business partners and opportunities to sell or license organizational analytics IP.