There has not been an exciting time than this to talk about data. Data is everywhere, it is being called the new oil, it has become a strategic asset. An organization’s success or failure is now quite dependent on whether it is able to exploit the business value of the data available to them.
In this long blog post, I am going to cover an approach for an organization or business to become data-driven. Why a data strategy is important? How we can align it to business strategy? Which areas to focus on to become data-driven? Which are the emerging technologies available to enable us in our data-driven journey? When to use which technology? Why data science is just a part of the whole puzzle? Why data governance is so important? How it enhances the business value of data? Which aspects of data governance are critical? Why data-literacy is important for business? How it can help to build data-driven culture? I will explore all these areas and will provide answers to these questions.
I plan to organize my blog posts under the following titles:
- Data-Driven, What & Why?
- Building Data Strategy
- Exploiting Emerging Technologies
- Applying Data Governance
- Building Data-Driven Culture
So let’s start…
Data-Driven, What & Why?
Countless surveys from many strategic consulting firms have shown how a data-driven business can serve its customers better, improve its products & get ahead of its competitors.
What is Data-Driven?
“Data-driven means that progress in an activity is compelled by data, rather than by intuition or by personal experience.” — Wikipedia
So becoming data-driven means taking decisions based on data, using data as an input to identify areas of improvement, getting insights to fix existing problems & improve performance. The data-driven organizations collect, analyze & get insights from data to identify opportunities & solve problems.
For business, becoming data-driven is about building tools, abilities, and, most crucially, a culture that acts on data.
Why be Data-Driven?
Earlier, decisions on improving performance or generating new products were taken either by the intuition of the leadership or the experience they had of the industry. But the possibility to take feedback or insights from available data has now changed the way of doing business.
“Most organizations this desire will be to better understand one’s customers and to improve products and services.” ~ Ernst & Young
“The organizations that were mostly data-driven had 4% higher productivity and 6% higher profits than the average.” ~ MIT Digital Business
“Data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain those customers, and 19 times as likely to be profitable as a result.” ~ McKinsey Global Institute
Over the last few years, the prominence of data has increased. While the 3Vs (volume, velocity & variety) of data have increased manifolds, recent advancements in technology (big data, analytics & cloud) have enabled many organizations to start or transform their business purely based on their ability to process & analyze available data to them.
This has challenged the traditional way of doing business, many of the businesses have transformed & optimized their processes taking the feedback from data. They have shown how the data available to them can be utilized to improve customer experience & products, optimize processes, identify problems/opportunities & even monetize the data itself.
More & more organizations are now trying to do the same, they are exploring innovative ways to collect, process & analyze data to transform their business to remain competitive or strive & thrive in their business.
Building Data Strategy
Let’s understand data strategy and explore how we can build it.
Data Strategy, What & Why?
“Strategy is a high-level plan to achieve one or more goals under conditions of uncertainty.” ~ Wikipedia
In a business, when an organization builds a high-level plan to achieve its goals and to get or stay ahead of the competitors, it’s called Business Strategy. As data has become a strategic asset in this information age, every business is going to be a data business. Hence when we integrate the insights gathered from available data into our business strategy, we call it Data Strategy.
Data strategy & business strategy has started complimenting each other, while business strategy can steer data strategy, data strategy can drive business strategy as well in innovative ways.
In a holistic way, as is any business strategy, a data strategy should be actionable, relevant, evolutionary & integrated.
So, why do we require a data strategy? Data strategy provides a centralized vision & foundation for data-related capabilities, be it identifying analytics opportunities, resolving data problems, or applying data management. As data has become a strategic asset, there has to be a data strategy to fully exploit its business value to stay relevant & competitive in today’s evolving business ecosystem.
How to build Data Strategy?
Depending on the type of business it is in, type of operations it performs, or type of data it has, different data strategies can be built for different organizations. In general, these are the areas that can be focused upon:
- Quick-wins: For any business, it really important to know or realize the ROI asap. So first priority can be to identify areas of smaller impact and turn-around. Based on the results, businesses would be more comfortable and eager to invest for longer terms.
- Improving business decisions: Identifying how business decisions are being taken right now and how available data can help businesses to make these efficient more efficient and quick can be other areas to explore.
- Improving operations: As more and more operations using technology, enough data is available to know which are parts of operations taking long to execute and how those can be optimized.
- Monetizing data: For some organizations, data itself can become a product and they need to identify and evaluate the ways to monetize it.
Above is not an exhaustive list of the steps that can be taken to build data strategy as its subjective to the kind of business an organization is in, the kind of problems it is facing, and the kind of opportunities it can identify with available data. Based on these factors, the above aspects of data strategy can be prioritized as well, i.e. for some businesses monetizing data can deliver more value than optimizing operations.
Exploiting Emerging Technologies
In this section, let’s understand emerging technologies and explore how we can exploit them.
Emerging technologies are a vast field (i.e. big data, data science, blockchain are separate field of study) and constantly evolving, I am just giving you a starting point and an approach to keep yourself updated.
Emerging Technologies, What & Why?
“Emerging technologies are those technical innovations which represent progressive developments within a field for competitive advantage.” ~ Wikipedia
Emerging technologies (i.e. Big Data, Data Science, Cloud Computing, Blockchain & IoT) develops an ecosystem that allows businesses to enhance their operations and maximize their reach to the customers with minimal overhead. Also, the fusion of these different wings unleashes a surge of new ideas for business innovations regarding workflows, methodologies, services, and products.
So why should we care about emerging technologies? Emerging technologies and their confluence has already started to disrupt the business ecosystems. There are businesses now that are in existence because of emerging technologies only, for traditional businesses as well, it has become customary to integrate these technologies into their processes. All this is generating a huge amount of valuable data which can be processed and analyzed further to get actionable insights that can provide any business a huge advantage over its competitors.
How to exploit Emerging Technologies?
Because of the rapid advancements in computing power and technologies, it has become necessary to stay abreast of the latest developments in the field of technology. Following are the aspects that can be looked into in order to keep a tab on and exploiting emerging technologies:
- Stay updated: Business teams on a high level and technology teams in-depth need to be aware of the evolution in emerging technologies. Big Data, Analytics, Cloud & IoT; what’s going on right now? What are the new fields emerging? i.e. Blockchain, Capsule Net, etc. What are the gaps they are filling in the industry?
- Know the use cases: In order to know the relevance of the latest development in technology w.r.t. to your industry, you should be aware of the use cases for these technologies. Most of the time it happens that these use-cases give you an idea of how these features of the technology can help you with your use-cases.
- *Assess the strengths & weaknesses: *As different technologies, products & vendors are providing the same functionalities or services, technology teams should assess the strengths & weaknesses of these features from each supplier and evaluate which one fits the best in their use-cases. i.e Hadoop Vs Spark, Azure Vs AWS, Cassandra Vs MongoDB, etc
- *Start with POC/Prototype: *In order to know the challenges & minimize the risk associated, technology teams should start with POCs or prototypes. Initially, it may look like an overhead but it will be wise to know the pros & cons with the minimal investment.
Applying Data Governance
In this section, let’s understand data governance and explore how we can apply it.
Data Governance is a field of study in itself, I am providing just an overview here, I encourage you to explore further and build your own understanding.
Data Governance, What & Why?
“Data governance is a defined process an organization follows to ensure high-quality data exists throughout the complete lifecycle. “ ~ Wikipedia
“Data Governance is defined as the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets. Data Governance guides all other Data Management functions.” ~ DMBOK
While business uses data as a strategic asset, there are certain aspects of data i.e. data quality, master data, data security which impact the business value of data. We need some framework & policies to manage these aspects which fall under Data Governance.
So why should we care about data governance? Imagine you have everything in place but data quality is not up to the mark, you don’t have complete or accurate data, you are handling sensitive data but don’t have a security framework in place. All these aspects affect the business value of data and need to be managed via data governance.
How to apply Data Governance?
As discussed above, data governance is all about having frameworks & policies to maintain data quality throughout its lifecycle. Following are the aspects related to data governance:
- Data Governance: central knowledge domain that connects all the other processes
- Data Architecture Management: to set rules, policies, standards, and models for data collected, used, stored, managed, and integrated within an organization
- Data Development: to manage data solutions in order to maximize the value of the data resources to the enterprise
- Data Operations Management: to manage data-related operations in order to maximize the value of the data resources to the enterprise.
- Data Security Management: to provide proper authentication, authorization, access, and auditing of data and information assets
- Reference and Master Data Management: to manage shared data to ensure consistency across the organization
- Data Warehousing and Business Intelligence: to manage decision support data for reporting and analysis
- *Document and Content Management: *to manage the lifecycle of data outside databases
- Metadata Management: to ensure proper creation, storage, integration of metadata (data about data)
- Data Quality Management: to measure, assess, improve & ensure fitness of data for enterprise
Building Data-Driven Culture
In this section, let’s understand the data-driven culture and explore how we can build it in our organization.
Data-Driven Culture, What & Why?
“A data-driven culture is a workplace environment that employs a consistent, repeatable approach to tactical and strategic decision-making through emphatic and empirical data proof.”
A data-driven culture is about setting the foundation for the habits and processes around the use of data. Data-driven companies establish processes and operations to make it easy for employees to acquire the required information but are also transparent about data access restrictions and governance methods.
So, why is it important to build a data-driven culture in your organization? The data can only take an organization so far. The real drivers are the people and hence building the culture around data is important. Analytics leaders must bring in the right talent, lead by example, and know when not to rely on data.
How to build Data-Driven Culture?
These are the aspects that an organization can work upon to build data-driven culture:
- Promote Data Literacy: To keep business & technology teams on the same page and collaborate effectively, it’s really required to promote data literacy for all. Both teams should know what the other team is doing and why to make the most of the data-driven approach.
- *Create Single-Source of Truth: *To eliminate data-silos, to provide the most accurate information at any given time & to democratize data access, an organization must create a single-source of truth.
- *Make Data Accessible: *Unless data is accessible to all employees, it’s impractical to expect the business to be truly data-driven. Employees should be skilled, informed & enthusiastic about using data to improve business.
- *Invest in Self-Service Analytics: *After doing all the above, still illusionary to assume an organization data-driven if most of the employees don’t have the right tools to make the most of available data. As not everybody can be a data scientist, hence there is a need to identify appropriate self-service analytics tools to empower business users.
Above is not an exhaustive list of steps to build a data-driven culture, but it’s enough business to get started and evolve in the future.
Ankit Rathi is a Principal Data Scientist, published author & well-known speaker. His interest lies primarily in building end-to-end AI applications/products following best practices of Data Engineering and Architecture.
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