Today, marketers have access to more customer data than ever before, be it demographic and socioeconomic data, information about customers’ preferences, purchase histories, spending behavior and much more. In fact, many marketers have access to more than they can comfortably handle.

At the same time, though, the advantages of harnessing all of this data continues to grow. In order to stay ahead of the curve in today’s ultra-competitive landscape, it is essential for marketers to embrace the data revolution. These exploding data sources should be used as a tool to inform insights and smart decision-making – and that holds true for everyone from small companies to huge multi-national enterprises.

The good news is that data can now be rendered in flexible, innovative ways to unlock key insights from existing information, whether that means analyzing campaign revenue and survey responses or tracking social mentions and assessing buying behavior. The key to making your data do more is having a foundation of agile management systems that handle streams of complex, often unstructured data from multiple sources and present it in an actionable format.

Here are three simple steps that all marketers should follow to make data useful:

1. Make it Accessible

When implementing data analytics software, it is important to keep the data and the software accessible for everyone so businesses can empower marketers across all levels of an organization with the ability to quickly drill down to specific details. In this respect, self-service data analytics are fuelling a democratization process. Marketers can make day-to-day decisions based on information garnered from analysis without relying on data scientists within the business or requiring deep knowledge of things such as databases, SQL or Hadoop. When employees can easily access data to show trends, progress and outliers, they’ll be more likely to embrace it as part of their everyday routine and decision-making process.

This move towards democratizing data is pushing the marketing industry to new heights, helping people across all levels of a business to intuitively manage campaign analytics in real-time, for instance. Similarly, it’s helping these same people deploy familiar data sources in ways that can be easily disseminated in a clear and visually compelling way, whether it be to internal colleagues within an organization or during customer-facing presentations.

2. Make it Visual

With visual analytics, marketers can more easily see and understand their data. Research shows that people can interpret visual information about 60,000 times faster than text (2012, Billion dollar graphics, The Power of Visual Communication). Accordingly, the ability to visualize data in charts and diagrams as opposed to having to scan through multiple lines of numbers not only saves time, but also allows marketers to interact with their data to discover answers that would otherwise be hidden.

3. Make it Actionable

Of course, accessibility and visualization on their own won’t do any good if they aren’t applied properly.

It’s important to build visualizations that inform and empower the business. Truly great dashboards help people make better decisions and inspire people to ask new questions. While there are many resources out on the web on designing great dashboards, here’s a quick guide on how to get started:

1. Make sure you start with important questions first, rather than jumping into data gathering. Think about it in the sense of what the key questions are that your audience needs to answer from the dashboard.
2. Stick to the few pieces of critical information. An important part of this process is to separate strategic questions from tactical questions. Separate these by audience (i.e. email campaign manager versus CMO) and deliver the right scope for each one.
3. Break down the questions into small, meaningful indicators. Start small, identify the minimum set of data required to answer the questions with the dashboard.
4. Think of dashboard development as an iterative process, where each new iteration brings new data and value to people.
5. To answer key questions, you may need multiple data sources, internal and external. For example, if you are a retailer looking to maximize sales of common products, you may identify what products to stock up on/place in visible areas by combining weather data with transactional data. Tesco recently did this to prepare for customers buying barbecues on warm weekends. With this insight, the retailer ensured both barbecue food products and corresponding equipment were well stocked in their stores during the summer and in the months leading up.

Keep in mind that strategic dashboards (for CMOs) and analytics dashboards (for analysts and people managing at the operations level) have different requirements.

For strategic dashboards, views should be clearly aligned with the core set of strategic priorities for the subject being addressed. For example, if a strategic priority for a product line is to increase repeat purchase, then have a view that is labeled exactly as that, in language typically used by the dashboard audience. Pointing out trends quickly, and the business targets and progress against those targets is what delivers the most value for this audience.

When designing analytics dashboards, they should typically include greater detail with a greater breadth of metrics. Assume they will be used daily or weekly, and make sure the data store support this level of updates.

The bottom line is that big data can lead to big value – but only if your analytics tools are implemented properly. Make sure you’re giving everyone access, giving them the tools to visualize, and using those tools to have a tangible business impact.

Elissa Fink is Tableau Software’s Chief Marketing Officer. With 20+ years helping companies improve their marketing operations through applied data analysis, Elissa has held executive positions in marketing, business strategy, product management, and product development.  Elissa’s also a frequent speaker and has spoken at conferences including the DMA, NCDM, PSAMA and others.