4 Key Components Necessary for Any Enterprise Data Strategy

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Most companies are aiming to become data driven. But there can be a huge gulf between aspiration and reality - one capable of determining whether a business performs competitively within its sector or falls behind other more data-savvy organizations.

Before jumping in headfirst and rolling out all-new data analytics tools, it's important to establish an underlying strategy. And, of course, it's also wise to make sure everyone is on board with what's happening; too many companies have watched their data dreams fall prey to lack of investment, or even outright resistance, from key stakeholders.

Here are four key components any winning enterprise data strategy must include.

Well-Defined Goals & Expectations

While there is certainly a lot of pressure on organizations to keep up with data leaders nowadays, wanting better analytics because it feels like you "should" or "have to" is not enough.

The best data strategies tie directly into specific performance goals - like minimizing customer churn, improving the efficiency of important processes, reducing operating expenses, tapping into promising new markets, etc.

According to Digitalist Mag, transformative data strategies fulfill these three conditions:

• The "what, why, when and how" are easy to communicate in plain business speak to all stakeholders across a company

• The strategy is focused on optimizing specific business outcomes, rather than trying to "boil the ocean"

• The strategy is able to gain support from executives and data specialists alike

Documenting goals in writing - as well as expectations and plans for your data strategy before taking any action - will give organizations a clear road map for moving ahead at the right pace.

The Right Data Analytics Platform

After clearly defining goals and expectations it's time for organizations to consider the data analytics system they will use to achieve them. Businesses running on a legacy analytics platform will likely need to upgrade some or all of their software.

Advanced platforms available today like ThoughtSpot offer many advantages over holdover tech, from improved artificial intelligence (AI) capabilities to vastly simplified data modeling. Having an AI-driven analytics engine in addition to a search-based analytics tool allows businesses to uncover more potentially valuable insights - those employees seek out specifically and those detected automatically by AI algorithms then passed along to human users. And whereas data modeling, or defining metadata, used to be a clunky manual process, it's now more of an optional add-on to make searching even more intuitive for users.

Evaluate every aspect of an analytics platform before deciding to deploy it, as this choice will affect business outcomes and the quality of the user experience.

Business & IT Partnership

Think about how your business will oversee and control data usage at every level. This is no small task, especially within a huge enterprise with thousands or even millions of users. Data governance encompasses everything from improving data quality across sources to assigning individual user permissions to access certain datasets.

Start by asking: Which teams are accountable for "owning" various tasks? What measures must your company enact to keep data secure and trustworthy? What best practices will define your approach to governing data? Who will be involved in data strategy decision-making?

Business teams need to understand IT requirements - like governance controls - while IT needs to ensure everything they do serves ultimate business goals.

Data-Focused Corporate Culture

Although company culture is less tangible than, say, data analytics tools, it's a very important component to bringing a data strategy to life. Corporate culture affects leaders' and employees' attitudes about data, which can in turn affect adoption rates and outcomes.

Developing a desired facet of company culture tends to go much smoother and speedier with buy-in from leadership, IT specialists, the human resources department and managers at every level. Consider the way your organization talks about data, sets expectations surrounding its usage and rewards data-driven decisions - then decide what needs to change to best foster your new and improved data strategy.

With clear goals, the right platform, a governance plan and a conducive corporate culture, organizations will be able to develop and execute a winning data strategy.