Dynamics Of Strategy In Technology And Data-Driven world

In the past decade, data science has continued to make rapid advances, particularly on the frontiers of machine learning and deep learning. Organizations now have troves of raw data combined with powerful and sophisticated analytics tools to gain insights that can improve operational performance and create new market opportunities. Most profoundly, their decisions no longer have to be made in the dark or based on gut instinct; they can be based on evidence, experiments, and more accurate forecasts. Not only the corporate but even political leaders across the globe have also recognized the importance of data-driven analytics for forming strategies.

To set the tone, Dan Wagner, CEO Civis Analytics (former data analytics chief of Obama’s campaign) quips: “Data is not a religion. It is not a panacea. Data isn’t going to what data you need to listen to. Humans are going to tell you what data you need to listen to. And, I think there is some kind of symbiosis in terms of executive leadership and strategy: what you are listening to, what predictions you are making with what levels of certainty around that information, and then what decisions you can make based on that observation”. You see a lot of major business decisions at the top corridors being made because of analytics at the epicentre. Indian major fashion retail e-commerce player “Myntra” adopting app only strategy at a certain point of time is prima facie of it.

As we take stock of the progress that has been made over the past five years, we see that companies are placing big bets on data and analytics. But adapting to an era of more data-driven decision making has not always proven to be a simple proposition for people or organizations. Many are struggling to develop talent, business processes, and organizational muscle to capture real value from analytics. This is becoming a matter of urgency, since analytics prowess is increasingly the basis of industry competition, and the leaders are staking out large advantages. Meanwhile, the technology itself is taking major leaps forward—and the next generation of technologies promises to be even more disruptive. Machine learning and deep learning capabilities have an enormous variety of applications that stretch deep into sectors of the economy that have largely stayed on the sidelines thus far.

Data and analytics fuel 6 disruptive models that change the nature of competition:

As data ecosystem evolves, the value will accrue to providers of analytics, but some data generators and aggregators will have a unique value.

The convergence of several technology trends is accelerating progress. The volume of data continues to double every three years as information pours in from digital platforms, wireless sensors, and billions of mobile phones. Data storage capacity has increased, while its cost has plummeted. Data scientists now have unprecedented computing power at their disposal, and they are devising ever more sophisticated algorithms. The companies at the forefront of these trends are using their capabilities to tackle business problems with a whole new mindset.  In some cases, they have introduced data-driven business models that have taken entire industries by surprise. Digital natives have an enormous advantage, and to keep up with them, incumbents need to apply data and analytics to the fundamentals of their existing business while simultaneously shifting the basis of competition.  In an environment of increasing volatility, legacy organizations need to have one eye on high risk, high reward moves of their own, whether that means entering new markets or changing their business models. At the same time, they have to apply analytics to improve their core operations. This may involve identifying new opportunities on the revenue side, using analytics insights to streamline internal processes, and building mechanisms for experimentation to enable continuous learning and feedback.

Organizations that pursue these two potential disruptors will be ready to take advantage of opportunities and thrive and they have to assume that those disruptors are right around the corner. Data and analytics have altered the dynamics in many industries, and change will only accelerate as machine learning and deep learning develop capabilities to think, problem-solving, and understand language. The potential uses of these technologies are remarkably broad, even for sectors that have been slow to digitize. As we enter a  world of self —driving cars, personalized medicine, and intelligent robots, there will be enormous new opportunities as well as significant risks not only for individual companies but for society as a  whole.

Potential data still untapped in  major  sectors:

Turning a world full of data into a data-driven world is an idea that many companies have found difficult to pull off in practice. Retail Industry in US markets has made the greatest progress.  In contrast, adoption is lagging in manufacturing, public sector and healthcare. Incentive problems and regulatory issues pose additional barriers to adoption in the public sector and health care.  In several cases, incumbent stakeholders that would have the most to lose from the kinds of changes data and analytics could enable also have a strong influence on regulations, a factor that could hinder adoption.

Legacy companies undergoing a digital transformation:

The relatively slow pace of progress in some of the domains described above points to the fact that many companies that have begun to deploy data and analytics have not realized the full value. Some have responded to competitive pressure by making large technology investments but have failed to make the organizational changes needed to make the most of them. An effective transformation strategy can be broken down into several components. The first element should be asking fundamental questions to shape the strategic version:

1. What will data and analytics be used for?

2. How will the insights drive value?

3. How will the value be measured?

The second element is building out the underlying data architecture as well as data collection or generation capabilities. Many incumbents struggle with switching from legacy data systems to a more nimble and flexible architecture to store and harness big data. They may also need to digitize their operations more fully in order to capture more data from their customer interactions, supply chains, equipment, and internal processes.

The third piece is acquiring the analytics capabilities needed to derive insights from data; organizations may choose to add in-house capabilities or outsource to specialists.

The fourth component is a common stumbling block: changing business processes to incorporate data insights into the actual workflow. This requires getting the right data insights into the hands of the right personnel. Finally, organizations need to build the capabilities of executives and mid-level managers to understand how to use data-driven insights—and to begin to rely on them as the basis for making decisions.

Analytics leaders are changing the nature of competition and consolidating big advantages:

There are now major disparities in performance between a small group of technology leaders and the average company—in some cases creating winner-take-most dynamics. Leaders such as Apple, Alphabet/Google, Amazon, Facebook, Microsoft, GE, and Alibaba Group have established themselves as some of the most valuable companies in the world. The same trend can be seen among privately held companies. The leading global “unicorns” tend to be companies with business models predicated on data and analytics, such as Uber, Lyft, Didi Chuxing, Palantir, Flipkart, Airbnb, DJI, Snapchat, Pinterest, BlaBlaCar, and Spotify. These companies differentiate themselves through their data and analytics assets, processes, and strategies.

The relative value of various assets has shifted. Where previous titans of industry poured billions into factories and equipment, the new leaders invest heavily in digital platforms, data, and analytical talent. New digital native players can circumvent traditional barriers to entry, such as the need to build traditional fixed assets, which enables them to enter markets with surprising speed. Amazon challenged the rest of the retail sector without building stores (though it does have a highly digitized physical distribution network), “fintechs” are providing financial services without physical bank branches, Netflix is changing the media landscape without connecting cables to customers’ homes, and Airbnb has introduced a radically new model in the hospitality sector without building hotels. But some digital natives are now erecting new barriers to entry themselves; platforms may have such strong network effects that they give operators a formidable advantage within a given market.

The leading firms have a remarkable depth of analytical talent deployed on a variety of problems—and they are actively looking for ways to enter other industries. These companies can take advantage of their scale and data insights to add new business lines, and those expansions are increasingly blurring traditional sector boundaries.9 Apple and Alibaba, for instance, have introduced financial products and services, while Google is developing autonomous cars. The importance of data has also upended the traditional relationship between organizations and their customers since every interaction generates information. Sometimes the data itself is so prized that companies offer free services in order to obtain it; this is the case with Facebook, LinkedIn, Pinterest, Twitter, Tencent, and many others. An underlying barter system is at work, particularly in the consumer space, as individuals gain access to digital services in return for data about their behaviours and transactions.

Value of data depends on its ultimate use, and ecosystems:

Data is at the heart of the disruptions occurring across the economy. It has become a critical corporate asset, and business leaders want to know what the information they hold is worth. But its value is tied to how it will be used and by whom. A piece of data may yield nothing, or it may yield the key to launching a new product line or cracking a scientific question. It might affect only a small percentage of a company’s revenue today, but it could be a driver of growth in the future.

Not all data are created equal:

Part of the challenge in valuing data is its sheer diversity. Some of the broad categories include behavioral data (capturing actions in both digital and physical environments), transactional data (records of business dealings), ambient or environmental data (conditions in the physical world monitored and captured by sensors), geospatial data, reference material or knowledge (news stories, textbooks, reference works, literature, and the like), and public records. Some data are structured (that it, easily expressed in rows and columns), while images, audio, and video are unstructured. Data can also come from the web, social media, industrial sensors, payment systems, cameras, wearable devices, and human entry. Billions of mobile phones, in particular, are capturing images, video, and location data. On the demand side, data can provide insights for diverse uses, some of which are more valuable than others. Many organizations are hungry to use data to grow and improve performance—and multiple players see market opportunities in this explosion of demand. There are typically many steps between raw data and actual usage, and there are openings to add value at various points along the way. To simplify, we focused on three categories of players in the data ecosystem, recognizing that some players might fill more than one role.

▪ Data generation and collection: The source and platform where data are initially captured.

▪ Data aggregation: Processes and platforms for combining data from multiple sources.

▪ Data Analysis: The gleaning of insights from data can be acted upon.

The University of Cambridge has coined a new acronym “DDBM”, stands for Data Driven Business Model. This model asks 6 fundamental questions for a data-driven business. There are a series of implications that may be particularly helpful to companies already leveraging ‘big data’ for their businesses or planning to do so. By utilizing the blueprint an existing business is able to follow a step-by-step process to construct its own DDBM centred around the business’ own desired outcomes, organization dynamics, resources, skills and the business sector within which it sits.

Data-driven businesses have been demonstrated to have an output and productivity that is 5–6 per cent higher than similar organizations who are not utilizing data-driven processes. An example of this is a recent article published in the Harvard Business Review, which provides five new patterns of innovation, three of which relate directly to data and its derivable. It is unsurprising that 71% of two banking firms directly report that the use of big data provides them with a competitive advantage – each often finding a slightly different angle to the data application. A business model for a data-driven business involves answering six fundamental questions:

1. What do we want to achieve by using big data?

2. What is our desired offering?

3. What data do we require and how are we going to acquire it?

4.  In what ways are we going to process and apply this data?

5. How are we going to monetize it?

6. What are the barriers to us accomplishing our goal?

Many companies today are striving to become data-driven. But what does that mean exactly?

More than just installing the right tools and applications, becoming data driven is about making data and analytics part of the business strategy, its systems, processes and culture.  It’s about creating a  mindset in which analytics form the basis of all fact-based business decisions, and are embraced by all levels of the organization.

Just like electricity, data has become a  basic enterprise asset that is quickly revolutionizing the world, enabling better, faster, cheaper business processes. Data-driven organizations are committed to gathering data concerning all aspects of the business.  By enabling employees at every level to use the right data at the right time, data can foster conclusive decision making and becomes part of the companies’  competitive advantage.