Analytics is the foundation of every digital initiative. Whether you’re in marketing, sales, service, finance, or IT, it’s likely topping your list of priorities for 2018. Yet, in a recent McKinsey survey of more than 500 executives, 86-percent reported that their organizations had only partially met the goals they’d set out for their analytics projects. These organizations are leaving some serious value on the table. For example, Bain found that "The Best" at analytics, those who had really tapped the benefits, are twice as likely to be in the top quartile of financial performance versus their peers.
But what did the "The Best" — those who comprise the top 5 to 10-percent of analytics adopters, do differently? It turns out they overcame a set of real roadblocks to success. Everyone wants to deploy analytics. But experiencing the thrill of being successful at deploying analytics isn’t an accident — it takes a plan, and executing on it.
It’s why we asked the International Institute for Analytics to put together a guide, "Revisiting Common Obstacles to Analytics Success." The IIA works with hundreds of organizations around the world, and summarized the common hurdles, and remedies, based on their work with analytics experts, academics and leaders at "The Best." The research stems from asking them a simple question: "How have you succeeded at analytics, and built real enterprise analytical capability?"
The Analytics Playbook Has Changed
In recent years, the playbook for success has required reinvention — because analytics itself has changed so much: A dramatic rise of platforms like Amazon Redshift and Azure’s various analytic databases has shifted data gravity to the cloud. Tools like Tableau and Power BI have moved analytics activities to teams outside of IT. Growing cloud sources alongside existing on-premise apps and databases, has meant an increasing variety of data sources to analyze and combine. A focus on customer analytics has seen a significant rise in the volume of data, and the need to connect the customer journey across application silos. A lot has changed — and it requires a new recipe book for turning analytics initiatives into business value.
In their research, the IIA, identified four strategic obstacles to success: business, execution, technical, and talent — each with three practical issues that come up repeatedly. Here are just some of the tactical tips they share in the guide:
Ask the Right Questions — Measure and Share Outcomes
On the business aspect for example, IIA note that it’s essential to identify specific business objectives, rigorous metrics and fact-based testing to ensure continued analytics momentum. For example, one large manufacturer found that their team focused too much effort on creating models and dashboards, and too little on asking the right analytics questions and applying it to achieve specific business results. The result was the executive team quickly lost interest — and the project stalled. Success means starting with the right questions, and regularly measuring business results to ensure continued engagement and investment.
Be Fast — Be Iterative
"Perfect is the enemy of good" — it’s easy for analytics execution to get bogged down, in pursuit of perfection. Often analysts want to delay full-scale testing of models and dashboards to incorporate "a few more enhancements," to get things just right. The problem is that projects can often get endlessly stuck in development, delaying value delivery. IIA advises setting a time limit on initial analytics projects, then immediately begin testing through real implementation, and as improvements or faults are discovered, go back and improve the original, then iteratively re-deploy it.
Pinpoint Data Obstacles
Technical issues often boil down to data. If you’ve been through an analytics project, then you know the drill. It’s not building the chart that’s the issue, it’s connecting it to data. You know you’ve hit the issue when you hear the question, "When can we get the data we need?" Or you end up with spreadsheet silos of data and uncoordinated inefficient" shadow data management." Consider assessing how analysts are spending their time, to identify precise data roadblocks. Establish explicit data roles such as data wranglers within teams to support the needs of analytics, which in turn can help ensure governance and repeatability if they’re all using a common platform.
Nurture Your Analytics Talent
The reality is that the need for analytics professionals is outstripping supply. So that means developing internal talent — which is often a better approach because they typically have the benefit of knowing the business, systems, and stakeholders. But after the talent has been developed, it’s also important to retain it. If people are bogged down in data cleansing, doing one-off data-extracts, or projects that aren’t delivering clear value, it’s a talent churn leading indicator. The IIA recommends creating a working environment for analytics professionals — ensure they’re networking with their in-house peers, investing in training in the latest tools and methodologies, creating career paths, embedding in business units, and creating blended teams to cover skill and experience gaps.
Now you’ve learned the four foundational keys to analytics success — business, execution, technical, and talent, and four common roadblocks within each of these areas to consider — and overcome. If you’re ready to learn the other eight roadblocks, download the new IIA research paper, "Revisiting Common Obstacles to Analytics Success" to speed your analytics journey.