Click to learn more about author Asha Saxena.

Most enterprise companies interact with, operate on, and leverage data across a vast array of business departments. Data is generated by everything from web apps to cameras to heart rate monitors to Internet of things (IoT) sensors, which is empowering richer insights into human and “things” behaviors. Companies that want to make the transition into being a ‘data driven organization’ may entail coordinating operational business decisions to a systematic interpretation of information by deploying Advanced Analytics. With the goal of becoming a digital business that uses analytic insights to capitalize and launch new business opportunities. Now you might think that it is quite obvious that companies would understand the importance of using data. But, you would be surprised how many organizations do not fully align their Data Strategy with their business objectives. 

How is it that some companies such as Netflix can spot technology trends and shifting consumer habits to make organizational pivots from a being DVD rental service to an online streaming subscription to finally becoming an award winning “original show” content producing internet TV juggernaut?  And others such as Borders Books or Kodak couldn’t foresee market changes and failed to adapt and innovate to stay relevant to consumers.  

I have previously taught a course called “Leading with Innovation: Align your Data Strategy with Business Strategy” on this exact topic. In this article, I plan to cover a few key elements that integrate data and innovation into Business Strategy.

• Understanding the key components of innovation
• Applying innovation into strategy based on business objectives
• Understanding key elements of Business Strategy
• Key elements in aligning Data Strategy to Business Strategy
• Building a use case for your real-world scenario

1. Understanding Key Components of Innovation

When you think of the word innovation, what does that mean to you? Innovation can mean a few different things; that may be defined as new products, devices, concepts, or systems, but it also can entail a new way of thinking creatively in order to make something better or easier. Innovation may be thought of big and small achievements. Innovation may also relate to reinventing existing business models, (think Uber to taxi industry) or adjusting to market changes to deliver more useful services and products. Many different organizations will have ideas on the key essentials, elements or components of innovation.

Karl Ulrich, professor of Entrepreneurship at The Wharton School says that it comes down to knowledge, culture and process. Whereas Mckinsey says it is comprised of 8 attributes of companies that excel at product, process or business model innovation. On the other hand, collaboration and diversity can also play an important role, since different ideas come from different minds. The consulting firm BCG put out a report on the Most Innovative Companies of 2018. To summarize, since 2014, there has been a surge in 4 types of innovation that all relate to digital: “Big Data analytics, the fast adoption of new technologies, mobile products and capabilities, and digital design.” The market leading companies of the study have found ways to incorporate digital innovations into their 3 core areas of function: strategy, operations and organization, to enable agile and lean development processes that effectively mold ideas into profitable growth. 

2. Applying Innovation into Strategy Based on Business Objectives

It’s clear that most companies need to have an innovation strategy, which means that it will align its innovation R&D efforts with its business strategies (market positioning and scope). Gary Pisano points out in the article “You need an innovation strategy” that there are really two kinds of innovation; there are “technological innovations” and there are “business model innovations.” Depending on your business industry, you may determine which type fits into growing your profits. Technology innovations may be perceived in terms of game-changing innovations such as Apple creating the first iPods and tablets, or a big pharma company such as Bristol-Myers Squibb shifting its R&D technology capabilities into biotechnology driven drugs because they thought it would help to improve the effectiveness of cancer drug products and keep market share against competitors. Business model innovation disruption has been quite popular over the past decade.

Companies like Uber, Netflix, Amazon, LinkedIn did not come up with any groundbreaking technology per se, but it challenged the existing industries legacy models with software systems that scaled. Google uses the business model strategy of giving away its Android operating system for free to phone manufacturers to disrupt and compete for market share against Apple iPhone products. Gary Pisano’s article also points out “routine innovations” such as Microsoft window updates or Intel processor upgrades. 

Depending on your business objectives, your organization may institute innovation in one or a few areas of the company. Identifying the business objective will help to assess how much of a budget or personnel will be needed for the initiative, what business issues will be solved, and how the innovation will contribute to your overall Business Strategy.

The bottomline is you need ask a few questions, “how will innovation opportunities create value for your customers and your business?” Next you need a detailed strategy for allocating budget towards innovation. And then senior management must realize that ultimately innovation business strategies must be tested against the actualities of markets, customer demand, technologies, regulations, and competitors for figuring out which ideas or products to continue or phase out.

While some business objectives may focus on innovation for smaller organizational processes such as changes in operating procedures, other strategies such as re-engineering may prove more costly and riskier, as other current processes may be disturbed and re-training may be needed, as well as investment into newer equipment or software, or even hiring of more staff.  As a consultant for institutions that take on data initiatives, one of the key components of taking on a data initiative is to assess the changes that may take place when implementing a data initiative.  In this instance, we would develop a strategic plan where the current processes are mapped out, figure out what adds or doesn’t add value, see what would improve efficiency within the organization or what would be easier for customers, and finally incorporate new technologies that may automate or improve the processes that have been identified.       

3. Understanding Key Elements of Business Strategy

Within Business Strategy, we can take into account two main ideas. The first includes Bruce Henderson’s economics of mass which depend on fixed-versus-variable costs (cost advantage), resources, awareness of competition and the ability to identify and apply unique strengths and capabilities. On the other hand, we have Michael Porter who postulated that the value chain activities performed by a company creates value which leads to competitive advantage. The application of both competitive and cost advantages may lead to building market dominance. This may be achieved through internal growth; mergers and/or acquisitions; new product development; expansion to new markets or contraction to focus on core competencies; price leadership, and/or re-engineering.

Internal strategies may also be applied to either gain economic advantage or add to the value chain. This may include downsizing, delayering, and restructuring in order to grow internally.  When developing a Business Strategy, it’s helpful to set mission statements, long term goals, objectives, core values, perform a SWOT analysis, leverage tools and techniques, understanding the macro trends, risks, and capability gaps with a business unit. 

4. Key Elements in Aligning Data Strategy to Business Strategy

Four principles of a successful Data Strategy includes asking the following questions:

• How does data generate value for the organization?
• What are critical data assets?
• What is the company’s data ecosystem?
• And, how do we govern our data?

When developing a Data Strategy, the use of a framework may allow stakeholders to assess each step involved in the Data Strategy process, such as taking into account business needs, current state, strategic imperatives, and finally an action plan.

Within the business needs the team may take into account the mission, objective and organizational structure. This will be important for assessing the broader mission of the data initiative and the people or departments that will be responsible for carrying out the work related to the initiative. Taking stock of what is currently available within the organization is imperative to seeing what can be used, what works, and what can be improved upon, and if there is a technology improvement or process re-engineering that will take place and how that will affect current processes and documentation. In this phase it will be useful to evaluate data from sales, profit, etc. used to evaluate the progress and success of the strategy and to inform of changes to the strategy in the light of that data. As an example, Accenture noted how the Oil company Chevron used data analysis of 5 million offshore oil wells to come up with a new way of horizontal drilling on shale wells that reduced the drilling time from 27 days to 15 day (which was a massive cost reduction).

5. Building a Use Case for Your Real-world Scenario  

The potential impact of using Machine Learning and Big Data strategies to extract business value from data to improve decision making on product development, R&D innovation and operational processes is significant. The key is using data analytics to uncover insights driven use cases that can be used to improve or solve critical business problems.  In the healthcare industry medical researchers use analytics to find genetic patterns that underlie certain diseases. The BCG group notes that “This data-backed insight led to the discovery and development of PCSK9 inhibitors, a class of drugs that lower cholesterol.”  The consulting firm Mckinsey found that the real life use cases can be broken down into 3 different categories: 

• Top line use cases: Improving customer facing activities- pricing, churn prevention, recommendation engines, promotion optimization
• Bottom line use cases: Leverage data-driven insights to enhance internal processes – supply chain optimization, predictive maintenance as well as fraud prevention. IOT applications and the data collected from it has spurred improves to business processes.
• New business model use cases: Is the sector of analyzing data beyond improving processes to using the insights to launch new portfolios of business offerings, services, offerings, products or services.    

But how do you go about collecting data for specific use cases? It is important that your company sets up certain requirements based on specific use cases to ensure that only relevant data is collected. Identify the business use cases that you want to explore, next you should formulize the “predictive models”, “smart variables” and necessary data to operationalize the use cases.  Some of these uses cases will need a significant “time series” of data to be analyzed, while others will depend on the “freshness” or real time access of the data.  There is a lot more details to the full process than this article covers but for further reading check out Mckinsey’s “Achieving business impact with data” Report. 

Allstate Insurance is good example of a legacy company that has shown a commitment to leveraging data as an enterprise asset to transforms its Data Strategy, technology and analytics to enhance its core vertical business activities further. Randy Bean, a Forbes columnist, points out that “Working in partnership with Northwestern University, Allstate is also employing AI techniques to better understand the history of policy holder interaction. By analyzing photographic images and mountains of text data, Allstate is able to detect additional signals that can predict policy renewal and result in a better customer experience.”  As I mentioned earlier that Netflix used Big Data and analytics to transform its business from DVD mailer rentals to jumping into streaming subscriptions.

The company began analyzing how their viewers watched shows and derived insights from noticing that many customers would binge watch one or two shows at a time. Which gave them the idea to run a algorithm prize contest, for software engineers that could “substantially improve the accuracy of predictions” for developing personalized algorithm recommendations to keep customers watching for hours on end and not cancelling the service.   The first “original show” they created was House of Cards in 2013, They committed 100 million dollars to making to 2 full seasons (before launching a pilot episode which is the traditional way a show gets made) all based on analytics and Big Data.  By parsing and analyzing millions of viewer data points; video plays, searches, ratings, browsing, scrolling, they were able to determine that fans of the original UK “House of Cards” series were also fans of Kevin Spacey movies that were directed by David Fincher and that political thrillers were a popular theme. Their Big Data driven decisions paid off on pushing into new business segments of producing original programming instead of just purchasing licensed content.  

Conclusion 

Every company should have the goal of evolving into a digitized and data-centric business. What is crucial for this to happen is understanding the need to treat data as “corporate asset” and maximize it as a source to benchmark and analyze their progress and core-competitiveness.  Data-centric organizations zero in on insights that may help with mining, cleansing, clustering and segmenting their data to gain a better understanding of their customers, influences, networks as well as product insights. Try to prioritize 2 to 3 best “uses cases” for building Big Data predictive models that will either be easiest to implement or will generate the largest business impact.

Data-driven companies also use Advanced Analytics, Machine Learning and AI to optimize business processes, functions and models.  All of this helps with finding and exploring new and disruptive business models that can lead to fostering growth and market relevance.