The number one thing you should do to build a prosperous enterprise data analysis strategy is to prioritize users over data.
To do this, you must first understand the goals and objectives for data analytics within your organization. The second step is to clearly and repeatedly communicate these goals and objectives to the diverse constituent groups within the business. Finally, the third step involves actively seeking input and participation of the constituent groups.
These critical steps will increase ownership of the strategy and accelerate its adoption, making it part of the organization's DNA.
This article will describe the process of prioritizing data analytics and how to make it a people-focused strategy by addressing the following key questions and points:
What is a data analytics strategy?
Identifying the business problems and goals
Answer why the data analytics strategy must support the core business strategy
Identifying the key stakeholders and influencers and the role of the "Champion" in engagement management and achieving buy-in by key stakeholders and influencers
Defining what types of data produce actionable information that enables accurate decision-making
The elements of an effective data acquisition plan
Why the best technology tools are required
Historically, the IT organization imposed a data management or data strategy on the business. The goal was to bring order, cohesiveness and business intelligence to the organization's eclectic sources of data.
That technological approach has failed to connect with end users, who don't know how to collect, organize, or analyze all that data. The average end user likes Excel spreadsheets and those magic graphs. And who doesn't enjoy creating an elegant pivot table for that occasional analytics project?
However, all those data silos stand as turf sentinels erected across your organization. For example, finance may adopt one solution while manufacturing and sales departments adopt others. In addition, shadow IT and legacy solutions—the aforementioned elegant pivot tables—remain embedded in the organization's data structure.
The result has been an exponential increase in inconsistent analysis between these silos and high overhead costs to sustain these analytics islands.
Maintaining all that data bloat is simply too difficult, expensive, and inefficient.
As previously mentioned, a successful data analytics strategy starts with delivering real-time, actionable information effectively and efficiently, enabling accurate and timely decision-making. The strategy should revolve around three vital components: people, processes, and technology. The strategy MUST have those components, or adoption will be low.
A data analytics strategy begins with the end-consumer (people), either internally or externally. Next, the strategy works back through the actions required to meet the user's needs (process).
Then, the appropriate data management and analytics tools (technology) must be used to meet both the consumer's requirements as well as business processes.
A data analytics strategy is like any other journey, so it requires a roadmap. But roadmaps are only helpful when you have a destination in mind. So often, in our zeal to begin harnessing that wealth of data, it is tempting to say, "We'll take the first step, and the map will evolve as we go."
But, if you don't have a map, you don't know where bridges need to be built, detours made, etc. Embarking on this journey without a goal (destination) is not a strategy, and it introduces high risk, wasted time, and often the expense of false starts or a failed project.
Defining clear agreement on the problem the organization is trying to solve is critical to a successful enterprise data analytics strategy. The best approach is to begin with, the end in mind!
Engage the active participation of the consumer communities the strategy is being developed to serve.
Initiate open dialogue and create a constant feedback loop among all strategy constituents—from front-line users all the way up to the C-suite.
Define what data quality looks like and what will be the success criteria.
Focus on the metrics that matter the most--don't chase "vanity" metrics that make the business look good or feel good but add no value to the bottom line! Vanity metrics don't uncover weaknesses, and growth rarely comes from measuring what you are already successful at accomplishing.
There is a classic proverb about matching goals with strategy: "When you're up to your neck in alligators, it's hard to remember that your initial objective was to drain the swamp."
So, deciding upon a solution to a problem does not mean that the solution meets your broader business goals. For example, your company doesn't have enough trucks to ship your product to market. One solution might be to produce fewer products to align with your shipping capacity. But suppose your broader business goal is to increase market share. In that case, this solution does not support that business strategy, and soon, the alligators that are your competition could gobble up your market share.
Alignment is the most important aspect of your data analytics strategy. You must ensure that your data analytics strategy is living and evolving with the business. Your strategy must include an appropriate, continuous improvement process that compares the strategy with the current business environment and goals.
This continuous improvement approach will require periodic tweaking of your strategy in keeping with the objective of data analytics: delivering actionable information in an effective and efficient manner for decision support.
The best business leaders know that what the "Boss" promotes, implements and measures is usually what everyone prioritizes. Because of this, a successful enterprise data analytics strategy must start with buy-in from high-level business owners that will then flow down to end-user cooperation and participation.
Its midpoint is the realization that data analytics is most valuable when it does one thing: it delivers actionable information effectively and efficiently for decision support.
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During the problem identification and alignment steps, it is vital to identify the key stakeholders of the strategy or who will benefit from it. You must get buy-in from each individual. These will likely be business leaders such as Directors or Vice Presidents of the end-user organizations that will benefit from the strategy by solving the daily problems they experience.
In addition to stakeholders, there are influencers across all levels of the organization. Influencers are the people that others look to or rely upon in department or business-wide activities. These are your daily cheerleaders for the strategy deployment.
Once identified, stakeholders and influencers must become invested and involved in the strategy, business alignment, and data governance process. The analytics team must be tasked with specific responsibilities through the strategy implementation. This inclusion creates a sense of ownership and will significantly increase the likelihood of successfully adopting an aligned and successful data analytics strategy.
Finally, data analytics adoption needs a Champion. The Champion is an executive-level individual who will ultimately be responsible for the success of the strategy implementation.
The Champion position is negotiated and accepted, not assigned as a "collateral duty." Meeting this voluntary Champion requirement during strategic planning and implementation will significantly improve operational planning and execution in support of the strategy.
The Champion must be a true believer and proficient in articulating, communicating, and advocating the transformational data analytics adoption for the organization.
The Champion must be a proponent of "engagement management" rather than traditional project management. Engagement management needs to be driven by the customer's success across the entire enterprise rather than the linear milestones and checklists of project management.
To produce actionable information for high-quality decision-making, you must map your strategy backward through the processes required to produce the output. Then, remembering the GIGO (garbage-in, garbage-out) rule, you need to critically evaluate the data source(s) required to fulfill the strategy.
In the data-rich environment of the big data age, a current mindset favors capturing and storing large volumes of data (or all the data you can). Sure, cloud storage is cheap, and the more data, the better, right? No! The truth is, actionable data is better!
The fallacy is that when you spend significant time processing, maintaining, and securing data not relevant to the business goals and business objectives, you produce no business value for the investment made.
Yes, "someday," you may wish to have that data. You can address that situation within your data analytics strategy when that day arrives. For now, you should marshal your resources and focus on the data that supports the present and near-future needs of the business.
Again, when building an actionable strategy, it is important to promote a collaborative environment. Stakeholders need to know what's in it for them regarding here and now and return on investment—not some future, intangible benefit of data analytics that is irrelevant to their interests and needs.
Your data analytics strategy must have tangible action items that support its value proposition and underscore its credibility to the business. Key among those is the data acquisition component of the strategy.
A comprehensive and efficient data acquisition plan is the most crucial component of a good data analytics strategy. Therefore, data acquisition should be a top priority when developing a data analytics strategy.
Without a constant flow of valuable data coming through the process and producing actionable information, the entire process is flawed. Therefore, critical considerations in data acquisition are the data's variety, volume, and velocity required to support the defined and agreed business goals.
Keep in mind how data acquisition works. First, identifying consistent and reliable data sources is required, and the assurance of access to those sources is critical.
A practical and successful data analytics strategy is only as good as the technology infrastructure that supports it. High-quality, actionable information delivered 10 minutes after the decision is made is of no value.
Most organizations have analytics tools already in use across the business. Therefore, evaluating those tools and their effectiveness within their silo is essential. Existing analytics tools can be a jumpstart to the strategy implementation, but ensuring a fit with the overall strategy must be the deciding factor in the verdict on a given tool's value.
It is important to remember that a multi-tool approach may satisfy factions within the organization, but this approach leads to long-term complexity and increased costs that eliminate any perceived retention value. The goal is to have a single analytics tool acceptable to the organization's broadest audience.
Great job! You've made it this far, so now what? There are so many factors that determine how an individual business will pursue a data analytics strategy. Still, the most important things to understand are what a successful data analytics strategy includes and how to approach developing one.
An additional benefit of implementing a data analytics strategy will be to reinforce with the business community the digital transformation being undertaken to convert your current organization to one that is data-driven. Remember, you cannot go from your "current state" of data analytics to your newly defined "future state" overnight. Developing a data analytics strategy is a process that requires long-term commitment and planning to accomplish.
The first step to developing a successful data analytics strategy is downloading the eBook, "10 Step Guide to Successfully Democratizing Your Company Data."