Your data reveals the difference between how you want things to be and how things really are – strategy versus reality.
At IBM, as part of the Cloud and Cognitive Software, we’re often asked to share the methods we use ourselves to help clients, across multiple lines of business, to comprehend the quality of their data and to build strategies and solutions that rely on data. We’ve developed this website in response to that request.
The main purpose of this platform is to share the framework we designed to help enterprises implement a decision portfolio that allows them to tightly link their business objectives to their data. Every business has objectives. Hidden in those objectives are unanswered questions, otherwise achieving objectives would be easy. We systematically assemble a prioritized, assessed list of these questions into a decision portfolio in order to innovate business models with data-driven strategies and, as a result, data-driven technologies and better achieve those business objectives. This methodology can result in billions of dollars of cost savings and new revenue for a given company. We have seen this in the real-world.
We believe that, by sharing the knowledge we collected so far by both designing and running workshops with this framework, we could endorse the conversation around new ways to capture the status of companies’ data in terms of business readiness. With this framework we hope to offer a way to reflect and look at data from a different perspective, with the aim to use it to design impactful strategies that can guide companies over the difficult transition toward a robust and successful digital transformation.
Starting from this core purpose, we then developed variations of the decision portfolio framework to accommodate different organizations’ needs. Reach out and talk to us on how to use these tools.
Design is an attitude to apply to problem solving. Design, when applied to data, is a very powerful, yet not often applied, technique to organize and discover insights. Design transforms data from raw material into actionable insights, by returning the visual representation of its inner structure and complexity.
Data is at the core of a successful digital transformation. Every company that wants to innovate and begin exploiting artificial intelligence to change its business should start first from an accurate understanding of the status of its data. In this regard, using design to visualize the flow of the conversation around data it is essential to extract meaning and comprehension.
Data design is crucial to discover overlooked insights into industries’ data: these insights reveal new business opportunities that can bring companies to achieve their strategic business objectives. The visual organization of information allows to display all the patterns and relationships existing within the complex systems in which data are being collected and organized within organizations. To visualize the tangled webs of connections that link data together it’s essential to unfold the potentialities buried into data and empower enterprises with new insights into their business.
Design thinking is an approach commonly adopted in a lot of industries today. At IBM we have been using design thinking for years. Placing end user needs at the center of every solution we develop is the goal. We embrace design thinking principles in the way we help clients find the right solutions to their problems and needs.
By merging data design with design thinking methodology, we create a framework tailored to craft innovative strategies that enable them to renew their organization by fully exploiting their data. If one then applies principles of design to visualize and understand data and uses that data to advise development techniques focused on end user needs then the result is a systematic approach to achieving objectives. Data Strategy is then, by definition, a data-driven approach that informs user-centered design to achieve business objectives.
Conventional approaches to strategy that focuses on traditional industry analysis are becoming increasingly ineffective. […] Instead of focusing on industry analysis and on the management of companies’ internal resources, strategy needs to focus on the connections firms create across industries and the flow of data through the networks the firms use.
(From Marco Iansiti and Karim R. Lakhani —Competing in the Age of AI, Harvard Business Review, 2020)
How do we then obtain a better understanding of the flow of the data within an organization? It’s to address this question and find a valuable solution to help both IBM clients and IBM teams, to succeed in their shift toward a full-fledge digital business, that we conceived of creating this new workshop’s framework.
Working side by side with clients across different industries made us recognize the importance played by an appropriate evaluation of the quality and organization of data. To begin modernizing a business with artificial intelligence and cognitive technologies, companies need to first fathom what data is effectively available within their organizations and how it is being collected and organized. By incorporating these steps, which are the initial rungs of what at IBM we call AI ladder, we started ideating a framework where data design and design thinking are merged together.
What companies need is to develop a new mindset that can spur innovation starting from their data. This methodology is specifically designed to extract value from data and use it to capture the digital maturity of a company. Having a framework that is easily adaptable and scalable on many levels of a business, is highly important to encourage organizations to start approaching the data they work with from a different perspective.
The activities included within this methodology lead to a visual representation of a company’s data, which unfolds hidden stories on how data are stored and organized. The activities allow teams in every company to bring to the surface unexpected insights, doubts, risks and questions relating to their data. This approach is useful to increase awareness of the potential hidden in data and to transition from a cloudy and old-fashion way to conceive and utilize it to a completely new and fresh approach.
Identify your team’s strategic objectives. Start by creating a scenario including all the opportunities your company has to overcome the challenges affecting your business today. Analyse and reflect on these opportunities to eventually extract a set of prioritized decisions that are needed to improve your organization and achieve your strategic business objectives.
Your prioritized decisions are linked to a series of activities focusing on evaluating your company’s data. These activities help discover how to leverage data to guide your prioritized decisions toward the solutions and technologies your company needs to implement. Through a visual analysis of your data, grasp new opportunities you can leverage to make your decisions.
Utilize the outcomes extracted from your data to craft a coherent and cohesive narrative laying out the strategy that your company needs to move forward with to achieve your objectives. Along with that, the framework also reveals a short term road map of the first solutions, such as AI or cognitive technologies, that you need to begin with in your journey toward digital transformation.
Cultural change is the most frequent cause of failure of transformation. When approaching a data and AI transformation, there is a strong need to turn the incomprehensible web of data into simple concepts. Every company has customers (Customer360) Products (Product360) and Talent (Talent360). Companies may have a few other conceptual data assets that are important to their business, these can be concepts such as spatio-temporal data (Location360), event streams (Event360), etc. However, there should be no more than 5-6. Data relevant to these constructs will need to be integrated into these assets as projects from the decision portfolio are executed.
These conceptual constructs of data provide concepts that anyone in the company can understand. They are a rallying point that can be referenced in and conversation of the transformation. Success is when anyone in the company understands data is an asset based on these concepts. The following steps happen in the context of these assets (described in this series of blogs: http://ibm.biz/6StepsUp, http://ibm.biz/Journey2Digital-Intro, http://ibm.biz/Journey2Digital-Part1, https://ibm.co/JourneyToDigital_Part2, https://ibm.co/JourneyToDigital_Part3, https://ibm.co/JourneyToDigital_Part4)
This framework allows teams to spend some time focusing on how their data is being collected and organized within their organization. Through data design your team visualizes data by giving shape to it by the adoption of different visual channels, such as color, size, proximity and position. Visualizing your organization’s data helps to represent the quality of its status, as well as of the status of the systems your company uses to store and access it.
Through data design your team visualizes data by the adoption of different visual channels, such as color, size, proximity and position. Discover some of the data design activities that we use to explore data.
This framework allows teams to spend some time focusing on how their data is being collected and organized within their organization. Try a few of the activities of our remote workshops.
Learn how we developed this framework: discover the AI Ladder concept and other learning resources to put in place a data strategy.
Beyond helping companies innovate through data-driven strategies, the realization of this framework is also the perfect example of a culture built upon pursuing innovation and finding new opportunities to experiment creative solutions.. These values are key to finding new solutions that can improve companies, and more broadly, humans’ lives, in this new world dominated by AI. The realization of this unique methodology, which has already helped numerous enterprises in their journey toward data and AI transformation, it's the results of the ideas, research, work and collaboration of the following people:
VP Data and AI, Chief Data Officer Cloud and Cognitive Software,
IBM Cloud and Cognitive Software
IBM C&CS CDO AI Insights Lead
IBM Data Science Elite Team -Program Director EMEA
IBM C&CS CDO Data Strategist