Data & Analytics Projects – The Recipe for Success

Kevin Emanuel, August 10, 2020

Over a decade ago when I first entered this industry, the concept of data and the perceived value of analytics were still unrecognized by many people and organizations around the world. Much of my career was spent explaining why analytics is imperative for operational efficiency and competitive advantage. Today, the fundamental concept and the value of data are known by all of us because well, everything we do both in our personal and professional lives is powered by data. From our smartphones and other mobile devices that feed us data 10s if not 100s of times each day to the smart appliances that we use in our homes, to just about every task we complete during our workdays, data is what powers our lives. Data is what tells us what we’ve done, how we’ve done it, our performance, and if we pay close attention it can also tell us where we’ll go next.  

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In our professional lives, we use analytics to enhance decision making, automate processes, predict outcomes, and extract business value from the data that we continue to collect each day. As businesses strive to become more analytics-driven and satisfy their hunger for the knowledge that is locked within the data that shape their organizations, true business value is only recognized by those who follow a few key principles in the implementation and deployment of their analytics projects. Here are some of the concepts and best practices that are proven to exploit the promise of data and analytics by delivering both effective and impactful projects:

1) Build a strategy

“By failing to plan, you are preparing to fail” – Benjamin Franklin

Planning and building a strategy is core to the success of every analytics project. When setting out on your analytics journey, the first step is to ensure there is a plan in place to align the objectives of the project with the company’s overall values and principles. Building a strategy involves defining both technical and business objectives for accessing the right type of data, orchestrating the most appropriate data architecture, identifying the appropriate technologies that provide the right capabilities, and designing a deployment plan based on input from managers across all areas of the business. 

2) Expand your vision

One of the fundamental mistakes in deploying an analytics solution is trying to deliver a solution for a single use case without understanding the value that it brings to the organization at large. While different business units or departments may operate in silos based on their unique objectives, data is what connects every functional unit to the core principles of the business. For example, while marketing’s primary objective may be to promote awareness and drive customers to the business, the ultimate goal is to drive revenues from the sale of products or services that are recognized by the office of finance. And while the finance team strives to ensure profitability and financial health of the organization, perhaps one of their objectives is to reinvest the earnings in Marketing or Operations to promote efficiency and growth. This is how data connects different business units across an organization and why it’s imperative that the project vision for all analytics projects must include an overarching plan to expand across the entire organization.  When setting out to build the business case for your analytics project, expand your vision beyond the use case at hand and ensure the larger business objectives are considered in order to maximize value and the business impact of your project.

3) Know your audience

When developing content to be delivered to your users, it’s important to identify the various personas in your audience and deliver analytics in a way that speaks to each group’s functional role. For instance, while your marketing team may prefer a visually appealing chart that displays high-level and graphical presentation of the data, your accounting or finance teams may prefer the data presented in numerical format using tables with various degrees of detail embedded. Identify your personas, interview each group in detail to gain an understanding of their unique requirements, present your findings and roadmap to the key stakeholders of each department, and once you have sign-off deliver the content in a way that speaks to your audience. Following this principle will ensure the adoption of your analytics tool as well as the content that will ultimately be delivered. 

4) Select the right tool(s)

As the analytics industry continues to grow, so do the options available for tools that can be used to deploy analytics solutions. Selecting the most appropriate tool that offers the right balance of governance, self-service capabilities, and technical features will be key to ensuring the objectives that are identified in the strategy phase can be delivered effectively. Identifying the type of metrics that need to be tracked, the readiness of your data, preferred methods of sharing data as well as your mobile readiness requirements should also be considered and can determine the most appropriate tools. Lastly, it’s important to realize that every requirement does not, and often times will not be met with a single tool. Depending on the organization’s analytic maturity and the state of available data, a mix of specialized tools can often result in more sophisticated and effective outcomes. 

5) Enable your users

Before an analytics project is ready for prime time, it’s important to ensure the personnel that are identified as users of the analytics platform at various levels (consumers, technical developers, administrators, etc.) are fully trained before the solution goes live. One of the primary factors that contribute to the success of a project and the adoption of a newly deployed application can be attributed to the readiness of the users in utilizing the new tools. Training strategies can be developed based on the size and the technical expertise of users. While smaller groups can often be enabled by structured training courses delivered by professional trainers, larger groups may require a “train the trainer” approach where key users are identified as program champions, trained extensively, and are then made responsible for training the larger groups within the organization. 

6) Monitor your progress

“You can’t manage what you can’t measure” – Peter Drucker

Analytics projects are almost never complete. As additional insights are gained from data and new business opportunities are unlocked, modifications and expansions are often needed to continue exploiting the full potential of analytics solutions. The effectiveness and speed in which an organization advances across the analytics maturity curve are fully dependent on its ability to monitor the progress and the positive change delivered both throughout as well as post project delivery. Therefore, it is imperative to incorporate ways to measure the success of an analytics project into the plan from the outset and hold key stakeholders from IT and the business accountable for measuring and reaching milestones. 

Conclusion

While there is no one right way to achieve success in deploying analytics tools, I believe the combination of the factors outlined here can result in the delivery of more mature and effective analytics projects.

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