Here’s what you’ll find in this blog:
- Part I: Identifying the Data Analytics Role in Digital Ecosystem
- Part II: Devising the Data Analytics Strategy
- Part III: Applying the Analytics
- How Lucrative.ai can Help
Apple Inc. was the first U.S. business to reach the $1 trillion mark back in 2018. A few months later, Amazon.com became the second U.S. business to do so, and then Microsoft in 2019. In January of 2020, Google’s parent company – Alphabet, became the fourth U.S. company to reach the same milestone.
So how are organizations redefining business success and accumulating greater wealth today than ever before in history?
The following blog explores the answer to this question through the lens of one of the most sophisticated tools available to companies today: data analytics. In particular, the focus lies on the implementation of data analytics in pursuit of business success.
As such, the blog is divided into three parts. The first part explores the concept of digital ecosystems and how they made data analytics increasingly relevant in today’s world.
The second part discusses a holistic data analytics strategy that organizations need to adopt to emulate the success of Apple, Amazon, Microsoft and Alphabet.
The third part discusses the application of analytics in modern businesses. This particular step in the data-driven strategy is a source of frustration for many SMEs and large enterprises alike.
Historically, companies have struggled to achieve a data-driven approach and implement advanced analytics to meet business goals. In a survey of 64 C-level executives from tech corporations such as Ford Motors, General Motors, and Johnson & Johnson, Harvard Business Review found that 72% of participants had not yet devised a data dominant strategy within their respective organizations.
Such a finding is eye-opening in terms of the stark contrast it represents between organizational cultures of competitive firms. On the one hand, Apple, Amazon, Microsoft and Alphabet have generated immense profits by adopting data analytics and unlocking the secret to its successful implementation. On the other hand, contemporary firms are either struggling to devise a data-centric strategy or have been unsuccessful in its application.
Thus, we take a closer look at data analytics strategy formulation and implementation in the 21st century. The rise of digital ecosystems serves as the starting point of this discussion.
Part I: Identifying Data Analytics Role in Digital Ecosystems
The true value of data analytics lies in understanding its role in the wider digital ecosystem of the 21st century. The ‘digital ecosystem’ is a network of newer, more innovative industries that produce consumer-centric digitally enabled solutions, accessible across industries. Think of an ecosystem as a system of transactions, flows and players, all connected to manufacture and deliver solutions to consumers. Such ecosystems have evolved because of the advent of the internet and digitization of services.
Now, ecosystems are digitally enabled and accessible for users and businesses alike. The relationships within these ecosystems, once unidimensional and unilateral, are now diverse and flexible. Thus, the rise of digital ecosystems has made it possible to measure and control information better than ever before.
And that’s where data analytics takes its root. The generation of Big Data and the tools to collect, sort and analyze it are a natural, if not an inevitable, consequence of the digital ecosystems. With advancements in artificial intelligence (AI) and machine learning, businesses now have the means to understand their consumers better than ever.
Data analytics helps identify key data-driven trends as well. Here’s how.
The digital ecosystems have also raised the expectations of consumers and stakeholders. With more sophisticated tools and a greater wealth of data at their disposal, corporations are now expected to deliver higher profits. This evolving mindset provides a glimpse into the mentality and organizational culture of the leading organizations in the world.
For example, Apple Inc. uses design thinking as the cornerstone of its business philosophy. The rise in design thinking is a result of the digital ecosystem in which Apple Inc. operates. The free flow of information through the ecosystem allows the company to identify consumer needs better than ever, as well as devise a strategy that caters to those needs.
With all the information at its disposal, Apple Inc. came up with design thinking to appeal to consumers and stand out from the crowd. As a result, the company has come up with revolutionary products such as the Apple AirPods, iPhone and Macbook. The development of these products is a result of the accessibility to information brought forth by the digital ecosystem and the accompanying data analytics.
However, the mere existence of a business in a digital ecosystem is not sufficient to garner business success. The accumulation, organization, measurement of data and the implementation of data-driven insights is based on a data analytics strategy. The most successful tech companies today are data-driven, with a well tailored data analytics strategy at their core.
The digital ecosystems provide a unique opportunity for businesses to devise and implement data analytics to understand their consumers in a whole new way. As consumers generate terabytes of data each day, the interconnectivity of data-centric systems can harness the power of this data, but only if businesses utilize a data-driven approach.
The following part of the blog explores the most effective data analytics strategy for businesses in the modern age, based on the success of the big four firms.
Part II: Devising the Data Analytics Strategy
A data analytics strategy allows businesses to focus their efforts when it comes to using data for business success. Devising a suitable data analytics strategy is never straightforward as it involves personalizing data techniques and analysis approaches.
As we shall discuss below, a commonly adopted data analytics strategy consists of five main steps. Of these steps, the application of analytical insights remains the main challenge for organizations today. As such, the application of analytics is discussed in further detail in the subsequent section.
1. Identify Value Sources
The first step in an organization’s data analytics strategy formulation is to consider the sources of value. This step involves having a deep understanding of your clients’ needs, and your organizations’ own core competencies. After all, the value sources are different for each organization depending on its own internal ecosystem that stems from the industry norms.
Firms adopt various methods to identify sources of business value. Chief among these methods is carrying out detailed market research to understand consumer and industry trends. However, within digital ecosystems market research manifests itself as real-time data generated by consumers and businesses. The abundance of this information is too vast for human intelligence which is why artificial intelligence (AI) has come to the forefront in the 21st century.
With machine learning models and artificial intelligence based systems, businesses are better able to identify sources of value within ecosystems driven by data generation. However, there is an additional dimension to value sources of business intelligence, and that dimension lies outside organizational ecosystems.
2. Think Outside the Ecosystem
It is important for businesses to understand the nuance of internal and external ecosystems. Only then would they be able to devise a suitable data analytics strategy that is appropriate according to the environment. Thinking outside the ecosystem involves taking intrinsic and extrinsic factors into consideration when collecting data and setting up organizational processes.
The advantage of broadening analytical perspective manifests in the evolution of company culture. Take Accenture as an example. Accenture was able to develop a data-driven strategy for itself as well as offer the same service to countless businesses around the world by taking external ecosystems into value consideration.
This step allows companies to put their own analytics capabilities and systems into perspective in terms of the wider industry trends. As such, the firm’s data analytics does not occur in isolation, rather becomes integrated with external ecosystems to retain relevancy.
3. Insight Modeling
Identifying new and innovative value sources and putting them into the context of external ecosystems is only half the journey. The rest of the strategy formulation process needs to be internalized for each organization. This internalization allows each company to develop insight modeling capabilities according to its own unique offerings, audiences, industry and organizational culture.
For effective insight modeling, companies need to consider the following:
- The establishment of modeling processes rather than modeling events. Too often do organizations fall into the trap of modeling insights for one-off data sets. Instead, the focus should be to establish processes that can be used repeatedly for different data sets.
- The prioritization of ‘actionable’ insights over ‘generic’ insights. All insights are data-driven at the end of the day, but some offer more effective implementation than others and these must be the priority.
- The structure to revise and revisit data modeling systems through the lens of artificial intelligence and machine learning for continuous improvement.
- And finally, the strategic contextualization of insights according to modern industry, consumer and business trends.
4. Applying the Analytics
Executing data analytics insights is the most challenging part of the overall strategy. As we shall discuss in detail later on, applying data analytics is essentially a five step process in and of itself. This process involves defining what ‘successful implementation’ looks like for your organization.
The preparation of data to extract an action plan from it, contextualizing the data, redesigning firm processes to adapt to suggested changes and finally a synergistic sharing of data to ensure proper data governance. Details regarding each of these steps is further discussed in part three of the report.
5. Controlling & Managing Analytics
The final step in devising a successful data analytics strategy is to establish systems of controlling and managing the analytics. No effective strategy is ever complete with an appropriate control and feedback system. Such a system allows businesses to track the changes in performance and make suitable adjustments in a timely manner.
It’s not all sunshine and rainbows. There are some key challenges in Data Analytics that businesses struggle with in 2021.
When it comes to data analytics, control and management take on a more sophisticated approach. Thanks to artificial intelligence, data learning models are smarter than ever. This helps the systems to analyze data for not just patterns and trends, but possible defects in the input. As such, data categorization and preparation in each subsequent cycle of analytics is improved. With the world relying increasingly on digital ecosystems and the data generated, this AI-based form of control and management is necessary to keep data analytics strategy relevant.
The implementation of the steps mentioned above is a good starting point for long term data analytics strategy. Now we shift our focus to the application of data-driven insights for businesses.
Part III: Applying the Analytics
Even in 2021, a data-driven approach among businesses is overlooked because of its intangibility. Despite the coronavirus pandemic accelerating efforts by top executives to integrate artificial intelligence and analytics within organizations, it is taking time. The main reason for this hesitance is the application of insights in a way that it generates tangible positive business growth.
As such, the following steps outline the basic structure to apply analytical insights for business success, based on industry trends and expert opinions. It is important to note that in order for insight application to be beneficial, it needs to be executed in accordance with the data analytics strategy outlined above.
1. Define Success Parameters
Once the data collection systems are in place and meaningful data has been gathered, it is time to understand the insights to implement them for business success. This means knowing what business success is for your organization. As such, defining the parameters of success is immensely crucial.
But this does not mean the same generic parameters already adopted by competitors. Nor does it mean emulating the success parameters specifically designed for some other business. Instead, the success parameters need to be uniquely distinct for each organization. That can only happen if the senior management considers the network of factors that play a role in the performance of a company.
The easiest way to understand defining unique success parameters is to understand what Michael Hammer said about strategy in his article ‘Deep Learning’. According to Hammer, the key to competitive advantage lies in devising a network of unique interrelated activities that cannot be replicated by competitors.
Much like the notion of a personalized strategy, what success means for your company cannot be the same as what it means for a competitor. Otherwise the application of insights will lead to a similar strategy and ultimately, a similar value proposition for both businesses.
2. Prepare Data
Once unique success parameters are set, it’s time to prepare the data. This step requires familiarity with several key concepts in data science, including:
a. Data Filtration
Data filtration consists of segmenting the acquired data into manageable and relevant categories for interpretation and understanding. The reason data filtration is necessary is because not all data is relevant to all companies. As such, firms need to filter data according to their own needs and identified success factors.
Data filtration is also essential in pinpointing the relevant target audience. The insights presented by data analytics are only applicable to a segment of the population. As such, this data preparation technique allows businesses to narrow down the segment on which adjustments are needed.
b. Data Sorting
Once data is filtered, it needs to be sorted according to importance for the organization. Data sorting is why it is necessary to establish success parameters in the beginning, so that it is easier to assess which data is most useful. In order to rank data according to importance, knowing what the organization is aiming to achieve helps the cause.
However, it is important to note that sorting data requires extensive knowledge of data science as well as organizational objectives. As such, it is best to develop a cross-functional team between data scientists and executives from each department to execute appropriate data sorting.
c. Data Grouping/Segmentation
Grouping or segmenting data allows big data clusters to be broken down into manageable chunks, thereby improving applicability of insights. As organizations enhance analytical capabilities with AI and machine learning, it is essential to structure the approach towards implementing insights.
That is why data grouping is a crucial step in data preparation. It involves summarizing data to understand and interpret holistic patterns among consumers. Grouped data is always easier to analyze, and therefore draw insights from, as compared to ungrouped data as it allows techniques such as cohort analysis.
d. Data Visualization
Perhaps the most important step in data preparation is visualizing data for company members. Not everyone is a data scientist or analyst, which is why there needs to be smart and efficient ways to present data already analyzed. Thanks to data visualization techniques, it becomes that much easier to prepare actionable insights because team leaders understand what needs to be done.
Corporations spend millions of dollars each year to devise data visualization tools for effective decision making. Yet, the most common challenge for data visualization is overlooking the main objective of generating actionable insights. Usually, companies get so involved in making attractive visualizations of data that they ignore whether it makes sense for key decision makers.
Interested in Data Visualization? Check out the Top 12 Data Visualization Dashboards by Lucrative.ai
3. Contextualize Data
Once the data is fully prepared, data scientists need to contextualize the data in terms of the business and the industry. Isolated data is one of the most dangerous traps for an organization. Far too often do businesses develop data analytics that are far removed from the real trends in the industry.
Moreover, such data analytics does not take into account core competencies for the firm either. If companies are to truly apply analytics for business success, the data needs to be contextualized to reflect real market conditions.
4. Redesign Processes
What most businesses fail to realize is that successful data application requires redesigning existing organizational processes. That’s because updated data requires updated processes to implement corresponding changes. In the beginning of the report, we talked about how Apple, Amazon, Microsoft and Alphabet utilize data analytics to achieve business success.
In each case, the corporations redesigned processes to better reflect the findings and insights presented by data analytics. This restructuring allowed more agility and flexibility in implementing the necessary changes to outstrip competition and meet consumer needs.
5. Ensure Synergistic Data Governance
Finally, the last step in applying data analytic insights is to ensure sharing of insights among and within organization departments. Without synergistic data governance, data stagnation occurs. That is when data from one part of the organization fails to reach another part. If effective application of analytics is to take place, it cannot occur in isolation.
Cross department sharing and collaboration needs to be a top priority for all corporations. It also enables better data governance and sound data management policies which pave the way for long term business success.
The true advantages of data analytics can only be realized with an effective long-term strategy in place. In order to emulate the examples of successful modern corporations, it is necessary to design personalized processes for insight modeling and application. As we move towards the new normal brought about by the COVID-19 pandemic, it is time to evolve business practices to reflect the digital ecosystems businesses thrive in today. The first step of that evolution is understanding and implementing data analytics for sustained business growth.
How Lucrative.ai can Help?
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