There has never been a better time to create impact with data & analytics. More and more data is available, computing power is increasing fast and analytical techniques are getting mature. Being data driven is the talk of the town, for sure it is part of the strategy of your organization. In the last decade, most companies have invested in data & analytics initiatives to enhance efficiency, increase sales and comply to regulation. Yet, these initiatives have not yet resulted into full business value. Organisations are getting ready for the next wave; getting value out of data & analytics products.
1. Climbing fast; the importance of data in value creation
Data is an asset and has (future) economic benefits. During the last years, the volume, complexity and richness of data has grown exponentially — mainly driven by e-commerce and Internet of Things or sensor data-and is expected to continue to do so (see also: McKinsey). In fact, so much of the world is instrumented that it is difficult to actually avoid generating data. We have entered a new era in which the physical world has become increasingly connected to the digital world. Data is generated by everything from cameras and traffic sensors to heart rate monitors, enabling richer insights into human and “thing” behavior.
Add to that the current growth in analytical power (e.g. analytics, machine learning, artificial intelligence). And the confluence of data, analytical and computational power today is setting the set for the next wave of digital disruption and data driven potential for growth.
“… the confluence of data, analytical and computational power today is setting the set for the next wave of digital disruption …”
This growth has a number of preconditions. Of course, organisations need to recognize that data is an asset. It also requires that required data is correct, available and (re-)usable. And potential revenue generation need to be qualified, e.g. through data marketplaces, data-as-a-service integration, digitization of customer interactions, product development, cost reduction, optimize operations and improving productivity.
In the last ten years data & analytics initiatives within organisations mainly focused on:
→ Controlled data & analytics, e.g. data organisation, data governance, privacy or trusted analytics;
→ Centralized source of available data, e.g. data platform or data engineering;
→ Insights value chain, e.g. deriving insights through use-case based machine learning, process mining or BI self-service by a team of analytics experts.
Not all initiatives bring the desired results. For example, deriving new insights is often considered as innovative, but any executive will recognize the sprawl of self-generated BI reports each claiming their own version of the truth, making it complex and time consuming to turn insights in to company steering. And although these initiatives are by-and-large based on business cases such as efficient reporting, comply to regulations or end-of-life for legacy systems, controlling and centralizing data & analytics, they are in fact supporting hygiene factors. And there is the trend that algorithms seem to be commoditizing, e.g. Google and Amazon are providing free access to their analytics libraries. In the end, this trend will transform any insights value chain into becoming a hygiene factor as well.
In any case, the results of most current data & analytics initiatives are not a breakthrough innovation or digital disruption.
2. Approach your data with a product mindset
So, while most data & analytics efforts are — still — performed to facilitate and improve the insights value chain, the real innovation is productization of data & analytics. Organisations need to look beyond their team of skilled data & analytics professionals with governed data sources, latest analytics tools and technologies if they want to leverage data to improve and increase revenue. To actively contribute to this, organization should start viewing data & analytics through a product development lens.
This means that we need to transform from data & analytics (point) solutions mainly focused on internal value towards the creation of full-fledged data & analytics products. Productization involves abstracting and standardizing the underlying principles, algorithms and code of successful point solutions until they can be used to solve an array of similar and relevant business problems. In the end, this should lead to a robust portfolio of data & analytics products.
To enable this organisations need to have the following foundation in place:
Bridge the gap between data & analytics and business – In many organisations, data & analytics and business execution are totally separate. The business lacks understanding of what is possible and therefore will ask for everything, without prioritization and lacking a requests funnel. This leads to the development of data & analytics point “solutions” without full business potential. Move beyond the current hype of ‘data literacy’ and actually involve relevant (business) stakeholders into data & analytics. Embrace change. And be practical by starting with data quality, ownership or relevant use-cases to improve daily operations through analytics, BI or robotics. Expand from there and be persistent. Truly and sustainably embedding data & analytics in an organisation is a long-term process.
Anchor data & analytics competences at executive level – Business impact from data-derived insights only happens when data & analytics is implemented deep within and consistently throughout the organization. This requires commitment, ownership, sponsorship and direction of a leader with the authority and sufficient understanding of data & analytics and its potential.
Understand potentials— the value of data & analytics depend on uniqueness and end uses
How to monetize the potential of data & analytics? Its value comes down to how unique it is, how it will be used, and by whom. Understanding the value is a tricky proposition, particularly since organizations cannot nail down the value until they are able to clearly specify its uses, either immediate or potential. Data & analytics may yield nothing, or it may yield the key to launching a new product line or making a scientific breakthrough. It might affect only a small percentage of a company’s revenue today, but it can be a key driver of growth in the future. General rule of thumb is that uniqueness of data will increase its value, so find that (hiddenn) gem. Where possible, join unique sets to further enhance the value and potential of data.
Product development takes an investment in time, people and technology. Set up a — technical — test-and-learn lab environment where pilots and beta-version products can be developed and by which the value can be further explored and understood. Include domain experts, data scientists, data experts. Capture client- and end-user needs in this lab environment and transform it into solutions and products. Identify quick wins for early adopter clients, to learn and develop how products work in a client environment. Set up a cooperation with sales departments and potential partners. Standardise, improve and scale products .
Take sufficient time — be lean where possible – Many organisations have invested resources and investments in data & analytics initiatives, e.g. hiring and/or educating data scientists, data lake implementations and data ownership. They are eager to finally monetize the data so that it indeed is ‘the new oil’. But if products are unclear or without market relevance, there is the risk of missing targets and being overtaken by competitors. At the same time, be opportunistic for quick results, perform pilots as much as possible to create an early adopters client base.
3. The coming wave: data & analytics product opportunities
Potential data driven product opportunities are well researched, identified and described. Think about e.g. IoT-based analytics for leasing companies and car insurers, real-time supply and demand matching for automotive, logistics and smart cities, personalized e-commerce and media, data integration between banks and B2B customers and data driven life sciences discovery. Besides, resolving the above mentioned foundation, a detailed approach on to realise these opportunities is less clearly defined. This paragraph contains the 5 main steps that all organisations should follow:
Step 1: Conceptualize the product
Identify a data & analytics product that meets the market needs within the lab environment. To identify relevant opportunities, include product expert, business groups (e.g. super users, sales and marketing) and -potential — clients. The process involves product definition and identification of data required for the product (which should include sourcing data creatively). Organisations with unique internal data have an increased opportunity to create highly valuable products with a good competitive edge. E.g. a bank with an agricultural background can use unique data which are highly sought after by other financial institutes. And a supply chain company can enhance their planning software with integrated robotics to increase efficiency for their clients, enhancing churn and sales opportunities. Take the uniqueness of available data into account early on in the process.
Determine the market position and potential business model for the prototype. There are three main prototype categories, i.e. a data-as-a-service product, algorithms code performing robotics and analytics & BI and software code containing interactive insights based on analytics algorithms.
Step 2: Acquire and build
Data as foundation for new products is traditionally captured internally and externally to support daily operations and reporting & insights. Given the vast amounts of data being available from commercial and public sources, extend the purpose of data acquisition for productization. Acquired data needs to be correct, timely, understandable, with a clear provenance — including restrictions for usage & storage and in accordance with regulatory compliance such as privacy laws.
To design and build correct algorithms supporting robotics and/or analytics products, an analytics pipeline needs to be established. In this pipeline correctness, reusability, bias, quality and provenance of algorithms and quality of code will be managed. Integrating CI/CD (continuous integration / continuous development) supports a lean and agile analytics pipeline with fast testing of the prototype value. Data and code need to be stored in an agile, scalable and secured environment. And finally, data & analytics products gain value from the context of their use, user interface and/or ease of use. So, incorporate UX design into the product development approach.
Step 3: Refine and validate
Once data is identified and (algorithm and software) code and user interfaces are designed and build, they need to be enriched, refined and validated.
Step 4: Readiness
Store data in an advanced environment where it can be integrated, queried, processed and searched. This makes data sufficiently, fast and reliable available for data-as-a-service products. Ensure that this is supported by a solid and robust data architecture. Distribution channels of algorithms and software code can be numerous, e.g. in a cloud environment where it can integrate with web- and mobile solutions. Custom made build into client environments. Or through pre-defined (API) connections made to measure for clients.
Step 5: Market and AI feedback
The competitive nature of the information product space, availability of new data sources and demand for timely decision support require an ongoing emphasis on innovation, pricing and monitoring product usage. Adding this step at this stage of the analytics-based data product development process is consistent with the iterative nature of product development in a “lean startup” context. Once again, the evolution of new technologies has provided a mechanism for facilitating a feedback and information extraction process from the marketplace
Brief recap: companies are eager to utilize the new data-oil. Not every organisation is able to do that successfully. By taking a comprehensive approach, persevering through sufficient knowledge building on ALL organisational level and starting small based on a step by step approach, you can be successful with data products and services.