It doesn't matter how well the data is stored, analyzed, and shared; it's about effectively using it to make informed decisions.
One of my earliest memories of my grandfather is him talking to my father about anticipating an increase in orders from a customer because recent demand had been trending upward. At the time, I couldn't fully grasp the significance of that conversation, but it all started making sense when I embarked on a career in data analytics. My grandfather, with his years of experience in business, had a keen sense of anticipating shifts in customer demand. He had likely meticulously studied the customer's order history and understood that they would soon require more of our products. This was 1992 with dot matrix printers, no charts, and lots of paper!
The concept of data isn't a new one. Regardless of a business's size or age, data has always played a role. However, in the past two decades, we've witnessed a significant shift as we've started capturing and managing data digitally. While data consumption itself is not a novel concept, our ability to handle the vast amount of data at our disposal has greatly improved. This surge in available data underscores the absolute necessity of having a well-thought-out strategy for its utilization.
As I highlighted in my article on the Data Value Trailblazer Framework, the true value of data lies in the decisions it empowers you to make.
Data-driven decision-making is the ultimate goal when working with data. It should be the cornerstone of your pursuit of success. Whether you're overseeing a contact center, involved in product development, orchestrating marketing campaigns, refining sales strategies, or managing overall business operations, having a precisely defined analytics strategy is paramount to not only thriving but also continuously growing.
In this article, I aim to achieve three primary objectives:
Define what an analytics strategy is.
Why is an analytics strategy important?
How can you evaluate my analytics strategy?
What is an Analytics Strategy?
An analytics strategy is a blueprint that guides how an organization collects, processes, analyzes, and uses data to make decisions that drive its business objectives. It's a roadmap to leverage data for business growth.
These are the key components and stages of an analytics strategy:
Objectives: Clear and specific goals that a company of a business unit aims to achieve through data-driven insights. These objectives should align with the overall business strategy.
Data Sources: Identification of the data sources, both internal and external, that will be utilized to gather relevant information. 3rd party applications, customer inputs, websites, apps, transactions, in-house applications, and sensors are top sources.
Data Collection: This may include customer data, order data, CRM data, operational data, and more. The data collected (referred to as extracted) has 3 categories: structured (think Excel spreadsheets with rows and columns), semi-structured (JSON, XML, Log files), or unstructured (text, images, audio, video).
Data Storage: All data extracted needs to be stored, Relational Database Management Systems (RDBMS) are widely used to store structured data, NoSQL Databases are choices for semi-structured and unstructured data. Cloud Storage solutions like Amazon S3, Google Cloud Storage, and Microsoft Azure are the most popular names today.
Data Processing and Transformation: Cleaning, preprocessing, and transforming stored raw data into a usable format so analysts can interact with it. This step is crucial for ensuring data accuracy and consistency.
Analytics: Using data to answer questions for data-driven 'decision makers' is the next step. Irrespective of tools, language, models, analysis, and algorithms used in this stage, the objective is to transform data into reports and insights that answer questions for anyone in the company who isn't an analyst.
Team and Skills: There's always a need to invest in hiring talent that can manage all stages of data (acquisition to enabling decisions). BI, Analysts, Data Scientists, and ML Engineers are common job titles.
Technology and Infrastructure: Tools to manage all stages of data - extraction, storage, transformation, data visualization, manipulation, etc.
Data Governance: Establishment of data governance policies and procedures to ensure data integrity, privacy, and compliance with regulations.
Monitoring and Evaluation: Reports and dashboards to answer 'What happened?' to monitor metrics, KPIs, and North Star help you evaluate how are you doing toward your business strategy and pivot/adjust at the right time. Analysis to help you answer questions like 'Why did x happen?', 'What will happen?' and 'What should happen to achieve ABC goal?'
Scalability and Agility: Consideration of how the analytics strategy can adapt to changing business needs, technology advancements, and evolving data sources.
Integration with Decision-Making: This is what it all comes down to. Questions answered and the decisions taken are the best ways to evaluate the effectiveness of your data and the teams you have.
An effective analytics strategy is not a static document; it evolves with the organization and the data landscape. It provides a framework for harnessing the full potential of data as a strategic asset, guiding organizations to make smarter decisions, optimize processes, improve customer experiences, and ultimately achieve their business goals.
Why Is an Analytics Strategy Important?
The biggest advantage of an analytics strategy is to have a good understanding of your data. Here are 5 capabilities that are unlocked with an analytics strategy.
Informed Decision-Making: In a data-rich world, decisions made on gut feelings or intuition are no longer sufficient. An analytics strategy empowers organizations to make informed decisions based on data-driven insights. Good decisions lead to growth in revenue.
Competitive Advantage: Organizations that effectively leverage analytics gain a significant competitive edge. They can anticipate market trends, customer preferences, and emerging opportunities faster than their competitors. This proactive approach is crucial for staying ahead in a fast-paced environment.
Cost Optimization: Analytics helps identify inefficiencies and areas where cost savings can be achieved. By analyzing operational data and processes, organizations can streamline operations, reduce waste, and allocate resources more efficiently.
Enhanced Customer Experience: Understanding customer behavior and preferences is essential for providing exceptional customer experiences. Analytics enables businesses to tailor their products, services, and marketing efforts to meet customer expectations, fostering loyalty and retention.
ROI Measurement: Every investment an organization makes, whether in marketing campaigns, product launches, technology upgrades, or process improvements, should be measurable in terms of return on investment (ROI). An analytics strategy allows for accurate measurement and assessment of the impact of these investments.
Analytics is not a one-time effort but an ongoing process. An analytics strategy supports continuous improvement by providing feedback loops, allowing organizations to learn from past successes and failures and refine their approaches.
How Can I Evaluate My Analytics Strategy?
To put it simply, if you're not excelling in ALL of the 5 capabilities mentioned above, there's an opportunity for improvement in your analytics strategy.
While, in my experience, the strategy is most effective when it is set and aligned from the C-Suite, it can also be established by analytics managers within a business unit.
Wrap-up
An analytics strategy is a compass that guides organizations through all stages of data. It helps you get the best value from your data and sets up a recurring cycle of continuous improvement. In an era where data is abundant and data-driven insights are paramount, an effective analytics strategy is the linchpin of success for any organization looking to thrive in the digital age.
Remember, in today's data-driven world, the difference between thriving and merely surviving lies in your ability to harness the power of analytics.
When there is a recurring problem, there should be a framework!
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