Chapter 4.pdf

Stuvia - Koop en Verkoop de Beste Samenvattingen Chapter 4: Turning Data into assets The role of data for businesses is changing dramatically today. Many companies that have used data as a specific part of their operations for years are now discovering a data revolution: data is coming from new sou...

Stuvia - Koop en Verkoop de Beste Samenvattingen Chapter 4: Turning Data into assets The role of data for businesses is changing dramatically today. Many companies that have used data as a specific part of their operations for years are now discovering a data revolution: data is coming from new sources, being applied to new problems, and becoming a key driver of innovation. Rethinking data From Data is expensive to generate in firm Challenge of data is storing and managing it Data is a tool for optimizing processes To (digital) Data is continuously generated everywhere Challenge of data is turning it into valuable information Data is a key intangible asset for value creation Data is valuable not just for companies like Google and Facebook. For any business today, data—like intellectual property, patents, or a brand—is a key intangible asset. The relative importance of that asset will vary somewhat based on the nature of the business). But data is an important asset to every business today—and neglected at our peril. Every business needs a data strategy Once you start to treat data as an asset, you need to develop a data strategy in your organization. That includes understanding what data you need as well as how you will apply it. The following five principles should guide any organization in developing its data strategy: 1. Gather diverse data types Gedownload door: matsmolenberg | [email protected] Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. ¤ 912 per jaar extra verdienen? Stuvia - Koop en Verkoop de Beste Samenvattingen 2. Use data as a predictive layer in decision making The worst thing that companies can do with data is gather it and not apply it when making decisions. 3. Apply data to new product innovation Data can power your existing products or services, but it can also be used as a springboard for imagining and testing new product innovations. ➢ For example, TWC’s Hailzone mobile app: TWC using its existing product data (for its TV shows and apps) to build a new service that added value for multiple customers (insurance companies and their insureds) 4. Watch what customers do, not what they say Behavioral data is anything that directly measures actions of your customers. Behavioural data is more valuable than reported opinions or surveys, because we humans are bad at remembering our own behaviour, predicting our future actions, or considering our motivations. 5. Combine data across silos Traditionally, businesses have allowed their data to be generated and reside in separate divisions or departments. One of the most important aspects of data strategy is to look for ways to combine your previously separate sets of data and see how they relate to each other. In putting together, a data strategy, it is also important to understand that many of today’s data sets are very different from the spreadsheets and relational databases that drove the best practices of data-intensive industries in the pre-digital era. Gedownload door: matsmolenberg | [email protected] Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. ¤ 912 per jaar extra verdienen? Stuvia - Koop en Verkoop de Beste Samenvattingen Big data = The entire nature of available data, and how it can be applied and used by business, has undergone a revolution in recent years. The impact of big data The phenomenon of big data is best understood in terms of two interrelated trends: 1. The rapid growth of new types of unstructured data Big data is really unstructured data: Traditionally, a firm’s data processes were based on analysing structured data—the kind of data sets that fill a database with neatly organized rows and columns. But the new big data has been marked by the profusion of new types of unstructured data—information that is recorded but doesn’t fit easily into neat forms. 2. The rapid development of new capabilities for making sense of this kind of data for the first time The second trend shaping big data is the rise of new technological capabilities for handling and making sense of all this unstructured data. If not for this, big data would be simply a giant haystack in which the needle of business insight might well be invisible. Fortunately, a range of technological developments is expanding our abilities to use the unstructured data that technology is producing. Big data on tap from the Cloud An additional trend is shaping the impact of big data: a revolution in the storage and accessibility of both data and data processing. - In the old data paradigm, for a business to manage data, it needed to invest in owned infrastructure to collect and hold all the data as well as any tools to analyze it. This significant capital requirement led to disparities among companies, with many unable to afford the sophisticated use of data. Today, businesses no longer need to store their own data, and even small businesses are increasingly able to access the leading tools for Gedownload door: matsmolenberg | [email protected] Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. ¤ 912 per jaar extra verdienen? Stuvia - Koop en Verkoop de Beste Samenvattingen using unstructured data. The reason is the rise of cloud computing. Three myths of big data: 1. The algorithm will figure it out 2. Correlation is all that matters 3. All good data is big data Where to find the data you need Finding the right additional sources of data is critical to filling in gaps and building your asset over time. Important sources of data from outside your organization include: 1. Customer Value Data Exchange - Invite customers to contribute data as part of interacting with your business or in direct exchange for value you offer them. 2. Lead User Participation - The lead users = The most active, avid, or involved customers. - Their greater needs lead them to have greater interest in interacting with your products or business, and they can often be a unique and powerful source of data. 3. Supply Chain Partners - Business partners can be crucial sources of additional data for building your data asset. - For example, airlines and the online travel agencies share only limited data. As a result, neither the agencies nor the airlines have access to the full picture of customers’ travel behaviors when they want to customize pricing and offers at the point of sale. - Increasingly, data partnerships will be a key element of how businesses negotiate terms of working together. 4. Public data sets - Another important source of new data is publicly accessible data sets. Some of these are in online public forums. 5. Purchase or exchange agreements - There are many opportunities for businesses to purchase or swap legitimate, valuable data with other firms. - Firms should seek out the many reputable services that enable anonymized data comparisons. Turning customer data into business value: four templates As organizations gather more data and develop it into powerful assets, the next challenge is to continuously apply these assets to create new value for themselves. - The following four templates are for creating value from customer data. 1. Insights: revealing the invisible - By revealing previously invisible relationships, patterns, and influences, Gedownload door: matsmolenberg | [email protected] Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. ¤ 912 per jaar extra verdienen? Stuvia - Koop en Verkoop de Beste Samenvattingen - customer data can provide huge value to businesses. Data can provide insights into customer psychology, can reveal patterns in customer behaviour 2. Targeting: narrowing the field - By narrowing the field of possible audiences and identifying who is most relevant to a business, customer data can help drive greater results from every interaction with customers. - Today’s advanced segmentation can be based on much more diverse customer data and can produce dozens of micro-categories (instead of only segmenting based on age, zip code, and product use). - Customer lifetime value should be included as one metric for targeting customers based on their long-term value to the business. - Hot spotting = identifying which of your customers are responsible for the most costs. E.g., 1% of a town’s population is responsible for 30% of the health-care costs of a hospital. By identifying this group via data, these patients can be “spotted”, and their care can be improved to prevent them from going to the hospital as often. 3. Personalisation: tailoring to fit - Once micro-segments of customers are targeted, the next opportunity is to treat them each differently. - By tailoring messaging, offers, pricing, services, and products, businesses can increase the value they deliver. - Keeping track of data generated by each customer allows for advertisement of the right product to the right customer. - One challenge of personalisation has been the proliferation of different devices and platforms where customers interact with a business. - How does a firm know it is communicating with the same individual on a phone, tables, and PC, its own shopping portal, or a display ad being served up by Google on pages all over the internet? 4. Context: providing a reference frame - By providing a frame of reference – and illustrating how one customer’s actions or outcomes stack up against those of a broader population – context can create new value for businesses and customers. - E.g., Nike customers who track their running data don’t just want to know how they did today; they also want to know how today’s performance compares to their own performance over the last week, to the goals they have set, and to the activity of friends in their social network (= context). Gedownload door: matsmolenberg | [email protected] Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. ¤ 912 per jaar extra verdienen? Stuvia - Koop en Verkoop de Beste Samenvattingen Tool: The data value generator The tool follows a five-step process for generating new strategic ideas for data. The data value generator looks at how to apply these concepts to generate new strategic options for data initiatives in your own organization Step 1: Area of impact and key performance indicators - The first step is to define the area of your business you are seeking to impact or improve through a new data initiative Once you have defined the area of impact, you should identify your primary business objectives in that area. Step 2: Value Template Selection Now that you know the domain you are focused on, look back at the four templates for value creation, and identify one or more that may be most relevant to your objectives: - Insight, targeting, personalization and context Step 3: Concept Generation After selecting a (or multiple) value template(s), you will want to use it to ideate specific ways that data could deliver more value to your customers and your business. Step 4: Data Audit - Once you have a strategy in mind, you need to assemble the data that it will require. Starts with surveying what data you already have that could be used to enable or power your strategy (current data). Next, you need to identify what data you still need for the purpose of the strategy (needs gaps). Finally, you need to determine ways to fill the gaps you’ve identified (new sources). Step 5: Execution plan The last step is to plan for the execution of the key pieces in your data plan. Gedownload door: matsmolenberg | [email protected] Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. ¤ 912 per jaar extra verdienen? Stuvia - Koop en Verkoop de Beste Samenvattingen Organizational challenges of data Often the biggest challenges are organizational, not technical. A number of common organizational challenges that businesses face when they shift to a more data-driven strategy: 1. Embedding data skill sets - The first challenge in the transition to a more data-driven organization is finding people with the right skill sets - Firstly, firms needs data experts, but… - But the data experts cannot be the only people in an organization who understand or think about data. In order to truly build data into a strategic asset, everyone in the business has to adopt a mindset that includes using data, and the questions they pose to it, as a part of their daily process. - The company may need someone who can connect the work of data science with that of the senior managers or the creative types in the marketing department. 2. Bridging Silos - In many organisations, divisions are reinforced by departmental silos and each department’s desire for “ownership” of its data (sales data vs marketing data, etc.). - Internal sharing as an obstacle: “the lack of sharing data in our organisation is an obstacle to measuring the ROI of our marketing”. 3. Sharing data with partners - Data sharing is critical not only within an organization; it is becoming a key element of negotiations with business partners. - Contracts and deals of all kinds are no longer just about who pays what to whom but what data will be shared as well. - This sharing is particularly important for businesses that don’t own the ultimate point of sale for their products. 4. Cybersecurity, privacy and consumer attitudes - As businesses gather and utilize more and more data, particularly customer data, they also bring on additional security risks - A security breach may not only be an IT problem, but also brand reputation issue (customers stay away if they fear their data is at risk of being hacked). - Part of data strategy is developing a legal, risk management, and security plan. Gedownload door: matsmolenberg | [email protected] Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. ¤ 912 per jaar extra verdienen?