Asking a business question these days immediately brings up a multitude of other questions that need answers first.

Where does the data to answer the question reside? Does it even exist? Maybe it’s stored in two or three different places. How will it get merged together? Should it be merged together? Is the data whole or is it missing key values? How will the answer wrap up into a story that the business questioner understands? How quickly can it be done? Ask any real data analyst and they will tell you these are just some of the questions that keep them up at night.

If one was to believe the majority of vendors out there these days, with the advent of “easy” visualization tools it takes no time at all to answer an important business question. Simply implementing a visualization tool on top of an existing tangle of systems—CRM/ERP back-ends, data marts, data warehouses, Web analytics, flat files, and on and on—can be a route to get quick answers. However, doing that does not change the key underlying challenges facing many companies, big or small: maintaining the integrity of their data as it moves across in-house applications and between different environments, the growth of analytical tools in the marketplace and choosing which one to invest in, and growing concerns about the complexity and redundancy of data repositories.

I have 20 years of experience working with data across many industries—including financial, retail, healthcare, marketing/PR, and nonprofit—from large corporate environments to small agile companies. I have seen the good, the bad, and the ugly of data management and I can honestly say that the only way a company will mature in its use of data as a means to drive its own business growth is by making sure the fundamentals of their own data management stay sound.

This article will outline the one major component that is fundamental to a successful data management strategy—an ongoing data audit. This will, in turn, successfully address and mitigate the underlying challenges each and every company has grappled with at one time or another: trust and accuracy of the data, understanding/communication of what data is available, and compliance and governance of existing data and implementation of new data collections.

Learn more: Read the entire article by downloading the Business Intelligence Journal (Vol. 21, No. 2).

Although more companies are starting to embed and operationalize analytics, respondents still face a number of challenges, many of which are related to people and processes rather than technology.

As one respondent put it, "IT, political agendas, and agreement on governance tend to be the largest barriers. Little is technical, mostly people related." These challenges are shown in Figure 7.

(Click for larger image)

People Issues

Lack of trust in the data or the results tops the list. Often people fear what they don’t understand. If the people making embedded analytics part of their business process haven’t bought into it, it is likely that the implementation won’t succeed. Respondents cited concerns about displacing people and power shifts that staff fear might occur if the organization becomes more analytics-to-action driven. Of course, there is also a lack of trust in the data that goes into the analytics. Forty percent of respondents cited this as a challenge.

Lack of skilled personnel also ranks high. The lack of skilled personnel who can implement and even utilize embedded analytics also ranked high on the list of challenges, cited by 38% of respondents.

No executive support, no budget. At TDWI, we repeatedly hear from organizations that the reason it is hard to get any analytics project up and running is lack of executive support. Some executives don’t have the vision and don’t understand the value. Some lack the knowledge of what analytics is all about. Often, analytics comes about when there is a change in leadership with a vision to deploy analytics. The budget issue goes hand in hand. About 28% of respondents cited these challenges.

Overcoming the People Challenges

What are respondents doing to overcome the challenges? Their responses naturally fell into several areas.

Starting small. Often organizations claim that slow and steady wins the race. Respondents spoke about "small projects owned by the business to prove the benefit/capability before pushing for a wider-reaching solution/business case." They spoke about taking "baby steps" to "solve problems consistently and gain trust" and "taking one step back to focus on people and process first."

Education. Skills ranked at the top of the list of challenges. To overcome this challenge, some talked about training from within because current employees understand the business. Others spoke about hiring externally and training internally. One respondent mentioned hiring a consulting company to do a POC and then that consulting company helps train the organization. Others are looking at tools that are easier to use. Past TDWI research indicates that organizations typically use a combination of approaches to build competency.

Communication. In addition to training staff to understand technologies, many respondents cited the need for educating their organizations about the value of embedding analytics. Often this involves socializing and evangelizing the concepts, especially if an executive is not yet on board. As one respondent explained, “We are using communication, demos, and actively engaging the business.”

Unfortunately, there is no silver bullet when it comes to overcoming people challenges related to embedding analytics. Some organizations are lucky in that everyone seems to be on board from the get-go. Often this happens in smaller or greenfield deployments in new companies that are growing quickly. Sometimes businesses have to wait until a new executive joins the organization to get something moving. However, many organizations spend the time to educate and communicate to get their analytics efforts moving. It is harder, but it can be done.

Read the full report: Download TDWI Best Practices Report: Operationalizing and Embedding Analytics for Action (Q1 2016).

Neutral stories are boring stories that have no impact. As a storyteller, it is important to show your voice and to take a position.

Don’t just state the facts—interpret them. Show your beliefs, your opinions, and the reasons why you think the story needs to be told. Don’t avoid conflict—acknowledge it and use it to engage the audience. Don’t be too cautious. Stay away from neutral language and style. Hiding your beliefs and emotions actually diminishes the impact of the story. If you appear to be a disengaged storyteller, you are sure to have a disengaged audience.

Make a considered and conscious choice about your storyteller voice and point of view. Choose first-person narrative when you, as the storyteller, are the focal point of the story and are describing your own experiences. First-person storytelling speaks of "I" and creates a very strong connection when it elicits visceral audience responses such as awe, fear, empathy, or respect for the storyteller.

Choose second-person narrative when the focal point is the audience and the storyteller perspective is that of message bearer. Second-person storytelling speaks of "you" and connects strongly when a compelling message draws the audience into the story.

Choose third-person narrative to focus on characters other than the audience or the storyteller. Third-person storytelling speaks of "he" and "she" characters by name. Bringing the audience into the story is a two-stage process. The storyteller shows emotional reaction—empathy, disdain, etc.—for characters in the story in a way that guides the audience to find their own emotional responses.

Read the full issue: Download Ten Mistakes to Avoid in Data Storytelling (Q2 2016).