As discovered in a recent study, 89% of business leaders think big data will transform business as much as the internet did.

In fact, 83% of those same enterprise leaders are now engaged in big data projects to gain an edge over their competition to deliver such benefits as using data to develop more impactful relationships with customers (37%), helping to redefine the development of products (26%), and altering the organization of operations (15%).

So how do you get decision makers like your CMO and fellow corporate executives to move from reading about data analytics to actually investing in it?

If your mission is to achieve buy-in for data analysis, then here are some guidelines to help tilt the scale in your favor.

And for more on this topic, read Econsultancy’s Multichannel Customer Intelligence Report which looks at how brands across the maturity spectrum have tackled the issue of integrating data into their organisations. 

1. Do your homework to build trust

It almost seems counterintuitive that you can use data to build something so intangible and downright emotional as trust.

Regardless of the department, whether it is marketing or sales, corporate executives are always concerned about people, money, and time.

To approach leaders about these matters, you have to prove that you have the same objectives as they do.

Be prepared and ready to show how you are using analytics to help your own department, your colleagues, and your internal and external customers.

Otherwise, people will be less inclined to believe you.

“If they can bear in mind that you both share similarities, the trust can be nurtured,” says Tomasz Wyszynski, director of data analytics at Schneider Electric.

In analytics, we just don’t do this often, and then we are amazed that people are not buying into our ideas, and this costs time.

Find ways to relate to the leaders so you can determine where and how to aim your efforts to show how data analysis can help them make better business decisions.

2. Do understand leaders’ decision-making processes

While ideas, emotions, and instincts can be volatile, data analysis is a methodology that can be used to help corporate executives make rational decisions grounded in reality.

The good news is that perfect data on an enterprise level is not needed to make a perfect decision.

In fact, Econsultancy’s Measurement and Analytics 2015 report finds 40% of companies feel the majority of their collated analytics data can be applied to drive decision-making.

Approximately, what percentage of the analytics data you collect is useful to your organisation for driving decision-making?

You have to learn how to ask the right questions and dig deep to find that sweet spot where data analysis fits in, as if you’re a journalist or a psychologist.

A great way to kick things off is by figuring out the three most important decisions executives make in their roles on a daily basis.

If you’ve done your homework, you will be able to quickly determine if their daily activities and decisions are in alignment with their objectives.

For example, Wyszynski recalls a meeting about customer analysis with a business director who was already in the midst of a price war he had initiated with some of his products at the time.

He was convinced it was the right thing to do based solely on instinct – he had not looked at one byte of data.

Wyszynski met with the director with the data in hand, simulating impacts on prices and volumes.

Their discussion opened the director’s eyes to the damage he had been doing to his business, and he adjusted his business strategy to a more reasonable path.

3. Do discover executive pain points

Getting buy-in is not easy; it’s a process that takes time.

Once you’ve built trust and learned about how your company’s leaders make decisions, you can uncover their pain points.

They typically present themselves as pushback. If you expect objections, then you will know how to address them accordingly.

One very common pain point is figuring out how to remain competitive.

As the aforementioned study shows, 79% of business leaders agree: Companies that fail to embrace big data will lose competitive market positions and could even go extinct.

Mike Messersmith, vice president in the sports nutrition division at NBTY, affirms that view:

Competition in every category is as fierce as ever, with incumbent brands seeking to maintain and grow their dominance and more new players than ever before.

Analytics and a commitment to measurement are critical in evaluating progress and performance. You can’t improve what you can’t measure.

Take note of the problems executives are facing and offer data analysis as a way to pull out the measuring stick and discover solutions.

4. Do adapt your message to your audience

Any experienced communicator will tell you it’s important to understand who your audience is.

That typically begins with speaking their language when you are presenting data analysis proposals to them.

Using big words and industry jargon like “market basket analysis” will leave their eyes glossed over.

It’s not that these executives aren’t smart; they’re quite business savvy. You simply have to use terminology that is easy to understand.

As another approach, Messersmith advises to lean in with a familiar angle.

For example, maybe your company uses big data in its audit or accounting departments, but has been slow to apply it to the marketing and sales departments.

If so, one specific tactic you can use is to show how you can support the audit department with analytics.

Talk to the lead of audit and say, “I know you’re running the audit for sales, and we have these algorithms for fraud detection, so maybe we could work together on this.”

5. Don’t forget about alignment

A common obstacle to getting buy-in with data analytics is the inconsistency in how data is presented and used by various departments in an organization.

Jennifer Kelly Dominiquini, chief marketing officer and client experience officer of BBVA Compass, has seen organizations where inventory and supply chain work against two different sets of metrics with incentives that are completely disconnected.

“They could be the most analytically driven people on the planet,” she says, “but if they’re measuring the wrong things, or measuring things that don’t match up to what others are measuring, you don’t have buy-in at all.”

It’s okay to start small and think big to make data analytics part of the DNA of a company.

If you keep your stakeholders focused on how data analytics can help departments and teams collaborate to prove or advance a point of view that is aligned with business objectives, their ears will perk up to what you have to say.

Dominiquini agrees:

If we use analytics to simplify to a set of metrics that we want to track against, it’s much better and much easier to gain alignment across different functions and levels of the organization.

6. Don’t go it alone

Recognize that you don’t have to launch a huge overhaul that will fundamentally shift the corporate culture.

Starting small lets you build allies for future projects.

By building trust, understanding who the decision makers are, and building alliances along the way, you are likely to increase your chances of getting corporate buy-in. 

Your presentation of the final analysis should not only be simple and highly visualized, but it should also clearly communicate everyone’s role so they feel like important participants in the program.

Spell out the implications for the CMO, for the product line manager, and for the supply chain manager – get everyone on board with the next action steps that each party should take based on the data to avoid presenting a blurry picture that results in nothing getting done.

7. Don’t complicate data analytics

Talented data scientists don’t just work on any and all business problems. 

They are selective and focus on resolving those issues that will provide the most value to the business.

Don’t just show up with an Excel document with a million rows of data. Leverage tools and presentation formats that are simple, impactful, and visual.

Dashboards and snapshots of data that help executives visualize KPIs tied to specific objectives will help you get closer to buy-in.

The key is to simplify your analytics to ensure the data is useable and provides insight that can be followed without viewing the sources as mutually exclusive.

All metrics can and should complement each other, regardless of source.

Default to asking yourself two questions: “Where is the money?” and “Where can I have a tangible impact?”

At the crossroads of these answers is where you should choose your battle.