Outsourcing Analytics vs DIY – Tips for Executives

If your organization has not yet embraced analytics, you may be wondering “what’s the best way to get started?” A key decision at the beginning is whether or not to bring in outside expertise to kick start the process, versus the traditional approach of recruiting an internal team. Another key decision is “which analytics software should we buy?” This post outlines some tips that executives can use to move forward.

Tip 1: Buying analytics software shouldn’t be your first step
There’s an incredible array of analytics software available in the market, many of which are marketed as turn-key solutions. The idea of an off-the-shelf solution appeals to a lot of business leaders … they are drawn towards the idea of a having a tangible asset that works right out of the box, without having to worry about the pesky people issues.

But, there are a lot of negatives that come with this approach:

The tool is only as good as the strategic thinking that goes into how it will be used. If you run an analytics tool on poor or incomplete metrics the tool doesn’t have a chance of creating business value.
The tool is only as good as the analyst running it. The analyst is the interface between the real business problem, and how that business problem is translated into the data and metrics in your system. If that translation is poor, then the tool is unlikely to generate powerful results.
The tool will quickly be discarded if it’s not generating business value. This will create a belief within the organization of “been there, done that … we tried analytics and it doesn’t work for us”. This can mean that your organization will fall behind the competition.

Tip 2: Think hard before recruiting from within
It’s not uncommon for an organization to build their analytics team with their existing staff. This approach increases the chance that your analytics team will get what your business is about, and hopefully they also represent the culture of your organization.

A challenge with this approach is that the team members who are recruited from within are often not able to give full attention to their new position, because they are still holding responsibilities related to their old roles. Another challenge with this approach is that there’s a risk of missing a big opportunity to take a fresh look at how the organization uses analytics to drive their key decisions. For example, if you recruit from your finance department, chances are that your analytics will be very financially focused. These concerns can be overcome, but it certainly helps to think about these considerations before making a decision.

Tip 3: Find a recruiter you can trust
If you’re building up a new team with external hires, getting the ball rolling can be tricky. Most organizations start by hiring the team leader, and then ask the team leader to do all of the following recruiting. A challenge with this approach is that whoever is hired first often sets the possibilities and the limitations of the team. For example if the first hire is a fan of traditional multivariate statistical approaches, chances are they will pursue analytics applications in that area, while leaving all other opportunities behind. They will create demand for their favorite analytics applications, and therefore hire other team members that have that same skills set (i.e. “he who has a hammer sees everything as a nail”).

So, the first hire with this approach is a crucial one, and given the specialized and nichy aspect of analytics, this will be a hire that you’ll likely do best to work with a recruiter that you trust. If you are successful in hiring a strong team leader, think about using the Who Method for setting targeted outcomes for the first 90, 180, and 365 days. These outcomes should reflect the business value that your organizations wants to get out of having its’ own analytics team.

Tip 4: Find an analytics consulting firm you can trust
The alternative approach would be to start off with an external consulting firm that specializes in analytics, and do a demonstration project with them. This approach is especially useful, as it allows you to start off with an experienced team and make progress quickly. This both increases the range of analytics that can be considered, and increases the chance of having a successful first project.

To get even more value out of working with an analytics consulting firm you can look at options for them to help you move towards building your own team. You can ask them:

Based on the work they do with you, can they build a “leave behind” tool that allows you to update the results yourself?
What insights do they have on your local job market for analytical talent?
Could they support you in building a recruiting plan?

Often leaders are hesitant to bring in an outside consulting firm because they don’t know what to look for, and they are worried about hiring the wrong firm, and/or asking for the wrong type of support. But what is less risky … hiring a consulting firm to do a “prove yourself” demonstration project, or building up a team of full-time staff with a completely new area of expertise?

Either way it’s generally better to focus on your people and processes first, and then afterwards, figure out the analytics software they need to do their job. Building an analytics capability in an organization takes a while. There are more things that can go wrong than go right. If you take a long term view, it makes sense to begin small (both with people and projects), realize some early wins, and gradually build the team based on the business value that they generate.

If you have stories about how you built your analytics team, please share them. And as always, please feel free to connect

Via our website: http://www.analysisworks.com

Via LinkedIn: http://www.linkedin.com/pub/jason-goto/2a/bb/a5a

Via Twitter: #analysisworks

3 Simple Checks to do Before Expanding your Data Team

If you’re the leader of a Data Team, chances are your clients are constantly demanding more and more services as time goes on. Your team members might be working longer days to keep up, and still you might not be able to meet all of the needs of your customers.

While the typical approach would be to try and get funding for more team members, there are other things that could be done first.

Here are 3 simple checks that you might want to perform before trying to grow your Data Team:

Check 1: What are the patterns of demand for your Data Team?
Despite being a numbers-driven group of people, it’s uncommon for Data Teams to actually analyze their own pattern of demand. Some questions to think about are:

How often do new requests come in?
Who do they come in from?
What is the nature of the requests?
What is the urgency and target turn-around time for the requests?
How long does it take to clarify the request?
How long does it take to deliver a result?

Getting a picture of your demand patterns will help you better understand what’s driving the level of busy-ness in your Data Team. It may point you in the direction of converting repeat requests into automated self-service reports. Or it may highlight those customers that have a chronic pattern of last-minute urgent requests, and in these situations you might benefit from proactively checking in with them once a week to see what might be coming up.

Or, at minimum, having this information will be your first point of evidence that your Data Team could benefit from having more team members.

Check 2: Is your team working efficiently?

As the leader of the Data Team you might be convinced that the team is working as efficiently as possible. But think about how you might go about convincing others. Some questions to consider are:

How many work hours does it take to respond to requests from your customers?
Does it take some team members less time than others to get things done? If so, what skills are teachable and transferrable between team members? Or, are there any team members that just aren’t pulling their weight on the team?
How much time does your team spend doing “disaster recovery”, meaning situations where some bad numbers have been released by the team, and they are scrambling to correct the numbers? If this is significant, then implementation of quality control measures like the Consistency Check can help.
What’s the percentage of time that your Data Team is doing mundane and repetitive work? This may point to the need to further streamline and automate your processes, and/or offer standardized self-serve reports for frequently requested information.
How many iterations (back and forth with the customer) does it take to complete a request? Are there opportunities to increase efficiency, by slowing down at the beginning, and getting clear on the what, when, why and how of the request?
What is the pattern of work hours for your Data Team members? Are they constantly working late, and if so, is it measured anywhere?

By attempting to answer these questions you, as the Data Team leader, may find that you have some easy opportunities to pursue before trying to seek funding to grow your team. Or alternatively, by answering these questions you will have the evidence to show that your team is working as efficiently as possible.

Check 3: When the Data Team can’t respond to requests, what does this cost your organization?

It’s very rare for a Data Team to keep track of the requests they can’t get to, which is a shame, because this can be invaluable information when thinking about expanding the team.

At minimum, a central log of requests can be set up, to track all requests that are made of your Data Team. The log should capture 1) requests that were accepted and delivered, 2) requests that the Data Team couldn’t respond to, and 3) requests that were accepted, but have been delayed by more than a couple of weeks.

Then, as the leader of the Data Team you could follow up with a sample of your customers that had unmet needs in the past year, and work with them to estimate the cost of not having that information. It doesn’t have to be anything fancy – even just a back of the envelope calculation is better than nothing. You may need to be creative, but you could consider the cost of making the wrong decision, or the cost of an adverse situation unfolding because of lack of awareness.

Pulling it all together, you now should know the pattern of your demand, the efficiency of your Data Team, and you should also have a rough idea of what it costs your organization when you can’t respond to requests. This will be the type of evidence that your leadership team can use to make an informed decision regarding whether to expand your Data Team or not.

Note: What is a Data Team?
When we refer to “Data Teams” it’s a catch-all for groups of technical, statistical, and subject-matter domain experts that are involved in providing information to support their organization. These teams are sometimes called “Business Intelligence”, “Decision Support”, or “Information Management”, but they can also be internal consultants such as “Operations Analysts”, “Strategic Information” or “Research”. Many of these concepts equally apply to teams of Data Scientists.

Are you reporting what you can? … Or reporting what you should?

Too many organizations are missing opportunities to use their data and analytics as a competitive advantage. Often leaders believe that having an abundance of data, reports, and colorful charts is what it means to be data-driven. But gaining competitive advantage through your data and analytics involves making sure leaders have the information they need when they need it.

But it’s extremely common for people in an organization to become complacent with the information that’s always been available, instead of demanding the data that will serve them better. The idea of “if all you have is a hammer, everything looks like a nail” is often evident in performance reporting … with too many indicators describing the same concept, while ignoring other critical parts of the organization.

To get some “out of the box” thinking it may be helpful to do an assessment every so often to make sure that the data and information is actually generating meaningful value to the organization.

Here are four questions to consider:

1) Does the current analytics help your leaders make decisions?
And, does it support them in taking the right action? If not, why? Take a hard look at the data and information available to the users with respect to:

Timeliness: Is information provided in a timeframe where users are actually able to take action? Or is the information so far out of date by the time it’s reported, that it provides little value for performance management?
Trustworthiness: Are leaders having difficulty trusting the data, because of accuracy and reliability issues?
Relevance: Are leaders having difficulty in seeing the relevance of the existing data? For example, seeing your current performance relative to past levels, or relative to industry standards.
Understanding: Do leaders actually understand the data or metrics that are being reported? Do they know where the data comes from, and what it represents? Are some of your metrics needlessly complicated? Is the data presented and charted in an immediately intuitive and visual way? Is it clear who is responsible for what? As best as you can, figure out what do your leaders really should know before taking actions based on the data and analytics.

If you have any of these issues, you’ll need to resolve them if you want to increase the competitive value of your data and analytics

2) Do you have too many metrics?
What are the few metrics (3 to 5) that cover the majority of what’s important, to the majority of the people in your organization? For example, if you have 20 metrics then think about how you can group them into topics and create aggregate metrics (i.e. like a GPA summarizes a student’s letter-grade performance across courses). Reporting dozens of metrics is easy, but not very actionable, because at most times some metrics will look good and some metrics will look bad. Slimming them down to the few metrics is a tough job, but when done right, it can be an incredibly powerful tool for communicating through the organization “what good performance looks like”.

3) What do your leaders want?
What would help them do their job better? Carry out working sessions with your leaders to figure out what the ideal data and analytics would look like, and more importantly, have them describe the actions they would take with this information. Ask them to think outside the box, and not restrict themselves to the data that they’ve seen before. Then ask your leaders to set priorities using the question: If you had to choose one metric, what would it be? If you’re doing this as a group working session, you will generate a lot of informative discussion, and a decent group of new metrics.

4) What is the ROI on the right metrics?
On your top-ranked metric for new data (i.e. data you currently don’t collect or have available), do a cost-benefit analysis. How much is it worth to your organization to have this information? Taking this approach can reveal opportunities to make a strategic investment in order to gain a competitive advantage. See Tip 3 from How to Get the Data You Need for more details.

If done right, this exercise will generate a burning need to make the investments to help leaders have the information they need, when they need it. And in doing so, empowering them to take meaningful actions based on truly relevant data and analytics. Translating analysis into action will create a competitive advantage for your whole organization.

Two Mega Trends: Big Data and the iPad … Where do they converge?

It’s no secret that Big Data is an emerging mega trend now and into the forseeable future. David Feinleib’s slideshare presentation on Big Data Trends shows a concise and current summary of where things are headed in the Big Data movement

Enter mega trend #2, the iPad. The current market share for iPads is strong and is projected to continue until 2016, according to the recent IDC study. I can say first-hand that most executives in our network are now in the habit of bringing their iPads with them wherever they go.

So if the leaders and decision-makers are about to be consumers of Big Data (they may not know it yet), and if they are all toting their iPads to their meetings, there must be an opportunity or two for forward-looking Big Data thinkers. This post is intended to start a conversation around the question:

If Big Data is growing like mad
And business leaders are using iPads more and more …
What’s our collective best guess as to …

… Where these two mega trends converge?

I’m sure this post will generate a decent discussion thread. To kick things off, I’ll put out my own thoughts.

There will be an increasing need to simplify the “so what” message
Tablet apps can be beautiful to look at, but they are rarely as successful when trying to pack a lot of information into a small space. Designers will increasingly need to give disproportionate attention to the “so what” message when reporting Big Data results.

As Lachlan James outlined in the recent post, on Top Business Intelligence dashboard design best practices intentional, effective and clear communication must be priority number one.

So if we agree with that idea, then instead of filling 90% of the reporting space with different charts and tables of results, perhaps the future way of iPad-friendly reporting would be like headlines in a newspaper, with catchy titles like: “We can accurately predict 80% of our adverse hospital events based on these 5 factors” or “65% of our customer retention in the Pacific Northwest and be explained by these 3 attributes”. Underneath the headline would be the supporting detail and charts.

This presents a challenge in automating the process of taking Big Data results, and explaining what they are saying in plain english. Perhaps there is a whole new area of opportunity here, with some links to artificial intelligence.

People will want to play
By the light-hearted nature of the iPad device, it lends itself to playing. Not that one would expect there to be a Big Data version of Angry Birds, but the concept of playing and interacting with Big Data seems like a likely user expectation. Perhaps as leaders interact with the summarized results of Big Data efforts, they will want to do things like:

Evaluate “what if” scenarios, such as “What if this pattern observed in this one customer segment applied to our whole customer base?”
Take an observed Big Data finding and forecast it into the future (i.e. If this trend continues, what will things look like 1 year from now?)
Play with different ways of visualizing complex Big Data results, using different charting tools, plotting symbols, colors, etc. (i.e. a techie version of “arts and crafts”).

Parts of the dashboard may be ever-changing
The nature of Big Data is that it is ever-growing and ever-evolving. Which means that “what was interesting and useful” today, might be taken as a given tomorrow. In addition, as companies use more or and more external data (as opposed to just using their own internal data) it may introduce another element of variability in terms of where the Big Data stories are. So, unlike previous BI and dashboard reporting efforts (i.e. with KPIs and measures that generally don’t change that much), the reporting canvas for Big Data may be constantly changing.

Translating this to the iPad experience, a core competency in reporting Big Data results through an iPad might be “the ability to educate as you go”. Leaders and executives will constantly be exposed to new findings and new measures, and they will need help getting up to speed regarding on what the findings mean. Conceivably, this may need to take place on the fly during the reporting stage, using popup videos or animations – using a broadcast email to communicate updates won’t likely cut it any more!

There will be an increasing need to simplify and track the “doing” step
As is often the case with reporting great results, nothing really matters if there’s no “doing” step. As leaders view the Big Data results in their iPads, they will inevitably get to a point in the meeting where someone says “We should do something about that”. The process of tracking “who is acting on what” will become more important for a few reasons:

Many people will see the results, but it might not be clear if anyone has started taking action. Nobody wants to duplicate efforts, but at the same time nobody wants to drop the ball.
There will be a lot of results, and a lot of actions to take, so if the full value of the information is to be realized then it’s important for there to be a means for tracking the actions.

The reporting of Big Data in the near future may be more like the Social Media experience and the Customer Relationship Management experience, with lots of communication and interaction.

I’m sure there are many people out there who know much more on this subject, so I encourage you to weigh in, whatever your point of view is.

Tips for Executives – Researching Your Local Market for Analytical Talent

As more and more articles predict a major shortage for analytical talent, many organizations are in a rush to quickly build up their analytical team. But, in the spirit of “crawl, walk, run” it never hurts to do some labour market research before launching your recruiting efforts. This homework will help your organization set more realistic timeline for building your internal analytical team.

Here are some tips that executives and leaders can use to research your local labour market for Analytical Talent:

Tip 1: Learn from other organizations in your area
Each region is different in terms of the local talent pool, so it’s a good idea to learn as much as you can from other organizations in your area that already have an Analytical Team. They can share their lessons learned, as well as, their recruiting and retention costs, and give you a sense of what it would take to build up a team in your organization. There should be plenty that you can learn from organizations in other industries, especially when you are just starting out.

Tip 2: Get advice from experts
There are many experts that can offer you advice on building an Analytical Team. Some potential experts include:

Recruiters that specialize in analytical professionals. They will be able to give you a sense of the analytical talent pool in your region.
A college or university with a well-recognized program in applied analytics will often be able to tell you where their graduates are being hired.
Consultants or consulting firms that actively specialize in analytical work. As service-oriented people they will likely be more helpful than you might think. Alternatively, you could hire them to help you with your recruiting campaign.

Tip 3: Check out your competition
Try reviewing the job postings for analytical talent in your area. It’s a pretty basic idea, but it’s still worth doing. You’ll find out:

Which companies are hiring, and how many openings there are
What they are offering to new job seekers, in terms of salary and benefits
How they are communicating to the talent pool
What job titles they are using
What level of experience, and credentials they are looking for

For example, if you go to a job posting site like Monster as a job seeker, and type in the keyword “data” and your location you will quickly get a good sense of your local market. When I ran this search today I found over 1,000 results in San Jose, but only 62 results in Boise, Idaho.

Applying these tips can save you a lot of time, and help you increase your odds of building your Analytical Team right the first time. There are many experts out there on this subject. Please feel free to weigh in with your point of view if you have something to add.

Tips for Executives – What to do Before Building Your Analytical Team

As the concept of using analytics as a strategic advantage is gaining more and more traction, many organizations are asking the question:

How do we get started building our Analytical Team?

In an effort to quickly catch up, some organizations make the mistake of hiring too quickly and firing too slowly. These situations can be avoided with a bit of strategizing at the leadership level. Here are some tips that executives and leaders can use to increase their chances of success:

Tip 1: Develop shared goals on why you want an Analytical Team

Most organizations that have Analytical Teams complain that their team is juggling so many different demands that they don’t use them as much as they would like to. The teams are busy, but the question is … are they busy working on the most important things? So before even building an Analytical Team it’s worthwhile for a leadership team to crystallize their top 3 goals for having a team. It’s strongly encouraged to keep it focused, because you can take it as a given that people will find new ways to use their talents.

Example shared goals might be:

To increase long-term customer retention by better understanding their buying patterns.
To support the leadership team in making major decisions using evidence-based methods.
To increase the cost-competitiveness of the organization.

It will likely require a brainstorming session or two to figure this out, but it is incredibly important ground work if you want to build your team right the first time.

Tip 2: Under each goal, identify one or two desired outcomes

To increase the clarity of what each goal actually means, next attempt as a leadership team to identify the specific outcomes that you’d like to target. These targeted outcomes would ideally be very tangible and expressed with numbers and an expected timeline. For example, if the goal is “to increase the cost-competitiveness of the organization” then some potential desired outcomes might be:

To outperform the industry average in inventory holding costs by 10% within 2 years.
To decrease in-warranty repair costs by $1m per year.
To increase operational productivity by 15% in three years.
To decrease the cost per customer acquisition by 10% on the next product launch.

The specific desired outcomes will often reflect the leadership team’s best educated guess, but that’s ok … the figures can be firmed up later, and in the meantime they further clarify the “what” and the “why” behind building an Analytical Team. You can imagine how this stage plays a big role in determining what talents and skills you will need for your team.

Tip 3: Estimate the value of achieving these outcomes

As shown in the previous example, it’s important to convert the desired outcomes into actual dollar amounts. This helps clarify how much opportunity the team believes is on the table. It also starts to paint a picture of what it’s worth to have the right analytical team. A safe approach would be to take the estimated total value per year from all three goals, and assume that 10% to 25% of them will actually be realized within the first 2 years. The resulting figure (total estimated value x 10%) will still likely be a much bigger number than you had planned to invest in building the team.

By using these tips, you can gain clarity on why you want an Analytical Team, the value you expect them to bring, and the cost of the team. By doing this pre-work you can significantly increase your chances of building the right Analytical Team the first time. In a future post, I’ll share some tips on how to recruit an Analytical Team.

There are many experts out there on this subject. Please feel free to weigh in with your point of view if you have something to add.

Tips for Leaders – Driving Change with Stories and Numbers

One of the biggest challenges that leaders face when driving change is getting everyone on board with the new direction. A powerful tool that Change Leaders can use is the combination of story-telling with numbers. When done right it can create the inspiration and momentum that both makes the change initiative happen and makes it stick. Here are some tips that leaders can use to get started:

Tip 1: Brainstorm the story
Chances are if you’re the Change Leader that you already know inherently why you want to drive the change. So the first challenge is “How do I transfer my excitement to other people?”

One of the best tools for getting people on board is the use of stories. Stories have the power to take boring, dry facts and make them personal and memorable.

A good source of relevant stories can be those that describe the frustration that people experience in the current state. These can be situations where things don’t work like they are supposed to, situations involving missed opportunities, or just things that are just plain annoying. A well-crafted story will be engaging and memorable, and inspire the listener to take some action. You’ll want to keep it short, because if things go right you’ll be telling this story over many times.

A major benefit of using a story is that the listener is more likely to remember it, and if the story is engaging then the listener will be inclined to retell it to others. Ideally you will have a story that will connect with the different types of people involved in the change, from the leadership team down to the front line, but if not, you may consider developing different stories for different audiences.

Once you have a few story ideas you can start thinking about the next step … finding the numbers in the story.

Tip 2: Find the numbers in the story
Many of your listeners will be on board after hearing your compelling story, but the more cynical listeners will say “That’s a great story, but it’s just an anecdote.” So the next challenge is finding the numbers both in the story, and the numbers that translate the story to the bigger picture.

When looking for numbers in the story, you may want to think about:

How bad was the situation? Can parts of it be measured and quantified? For example, if the story is about a situation where a customer was dissatisfied about a long wait, how long was the wait? To put it in context, how much longer was the wait in comparison to the industry standard?
What efforts went into fixing the bad situation? Did the bad situation result in many different people getting involved? If so, how much time did they spend? For example if the dissatisfied customer spent time with the manager, then with customer service, and then finally escalated the complaint to the leadership team, how many hours of effort went into trying to fix the situation?

When translating the numbers in the story to the big picture, you may want to think about:

How often do situations like this occur? Is this a one-off, or does this problem repeat itself every day? If the bad situation occurs frequently, what is it costing your organization?
If you don’t know how often this occurs, how frequently does it need to happen for it to be important? For example, in situations involving a person’s safety, one bad occurrence might be enough for it to be important.

Now that you have the story, and the numbers that back it up, the next step is to connect it back to the change you’re driving.

Tip 3: Make the hero of the story be the change
In the best stories the main character faces a challenge that seems impossible, and then somehow figures out how to overcome that challenge. The hero can be the person who came up with the bright new idea, or even better, can be the improvement idea itself.

As the Change Leader, you will want to find the connection between your change initiative and how the hero of the story overcame their challenge. For example, if your change initiative is about reducing wait times for customers, and if your change initiative involves a new screening process to identify customers with complex requirements, then the hero of the story can be the bright team member who thought of the idea, and the manager who was willing to try it out to see if it would work.

Tip 4: Performance manage with the story
You can take your story-telling even further by linking it to your performance management. It can be as simple as tracking the key numbers in your story on an on-going basis, and setting performance targets around them. Tracking tools can range from a good old fashioned white board to a fully automated electronic dashboard – the main thing is to measure what’s important, and to have the discipline to stick with it.

As you review the performance measures with your team, take every opportunity to refer to the characters of the story, and the situations that they went through. This will remind the people involved in the change why this is important, and will also help get new members of the team on board as they hear the story for the first time.

If you’re a numbers person, you might not have much experience with telling stories. If so, a great resource on storytelling is Peter Guber’s “Tell to Win”. His book describes the important components of any memorable story.

Hopefully these tips will help Change Leaders use the powerful combination of story-telling and numbers to drive change. There are many experts out there that I’m sure will have more to add. Please feel free to weigh in with your point of view.

Tips for Executives – How to Create a Culture of Evidence

We’re often asked how do we create a Culture of Evidence? Most leaders know that they should be more evidence-based in how they work, but don’t know how they can go about doing it.

We’ve all heard the phrase “Culture eats strategy for breakfast” and anyone whos attempted to drive change in a complex organization knows how true that statement can be. And, many seasoned leaders know that culture change doesn’t happen overnight, but here are some tips that you can use to get started.

Tip 1: Paint a picture of “What a Culture of Evidence looks like”
If you want to make meaningful progress towards creating a culture of evidence, there’s no better place to start than envisioning your future state. Things to consider include:

How will life be better? For you, your team and for the company?
What opportunities will you be able to access?
What risks will you be able to avoid?
What decisions will be smarter?
What time will be saved?

If you can create a compelling vision of your organization in the future that thrives in a Culture of Evidence, then you can use this to win supporters.

Tip 2: Set the standard for “What counts as evidence?”
In the spirit of “crawl, walk, run”, getting started with using evidence doesn’t have to begin with hiring a team of scientists, researchers and lawyers. To begin with it may be as simple as using data to support your decision-making, carrying out basic research, or using spreadsheets to do “what if” analysis. Most leaders do this already, but many others still rely on their intuition to make their decisions.

The following is an illustrative example of “what counts as evidence?”:

A declarative statement of your position such as “I believe that we should launch a social media awareness campaign for our red widgets”
Some form of objective proof that shows how you formed your position, such as “According to our market data 85% of our target customers have never heard of our red widgets, and 57% of them use social media. The campaign would be cost effective even if it only generated a 5% increase in our market share.”
A disclosure of what you don’t know, such as “Admittedly our market data is one year old, so we’re assuming that the patterns still hold.”
An action statement, such as “I’d like to update our market data but the delays and costs outweigh the risk of missing an opportunity … I recommend that we launch the campaign and track performance.”

The ultimate goal of evidence is that it holds up to the review process, meaning that another leader could review the evidence and arrive at the same conclusions. Along those lines, “what counts as evidence?” could be just that … an objective analysis that has been peer reviewed.

Tip 3: Put the tools in place
To set your team up for success, you will want to make sure that the basic tools are available for evidence-based thinking. Some questions to consider include:

Are the right investments being made to collect the right data?
Does your team have access to the data they need? Is the data being collected at the source, but it’s not being stored in the data warehouse? Or is the data there, but the privacy levels are too restrictive?
Do they have the skills for working with the data, or alternatively, is the right information available in insightful reports or visual dashboards?
Do they have the right technical and human resources perform deeper analyses, in response to important business questions that arise?

Tip 4: Lead by example
If you want to convince your team and your peers that you are fully behind this idea of a Culture of Evidence, then you’ll need to walk the talk. This will require effort at the beginning, but after a while it will become just “the way things are done around here”. Leading by example can include shifting your own language from “I think this is what we should do …” into “The evidence tells me that this is what we should do …”

It can also include making a concerted effort to not do things the old way because “that’s the way we’ve always done it” but instead doing things in ways that are proven to generate the right outcomes. This relates to everyday decision-making and operations, as well as longer-term strategy and planning.

Tip 5: Reward the adopters
It is often said that “you get what you reward”. This is an easy concept to apply to building a Culture of Evidence. For example you can reward your team for using evidence in situations like:

Decision-making on special projects: Projects that have proposals that have supporting evidence are often approved, whereas other projects often don’t.
Decision-making on budget: Budget increases (or exemptions from budget cuts) are generally provided to those departments that can prove that they need it, whereas departments that can’t prove their value miss out.
Decision-making on promotions: Team members that demonstrate the effective use of evidence are generally promoted to higher positions, whereas other team members don’t.

By taking this approach it won’t take long for people in your organization to learn that the way to win is by embracing an evidence-based approach. Team members will either adopt the new direction or self-select themselves out of your organization. Over time this will increase the momentum of the culture change, and gradually you will find that your organization attracts talent that values a Culture of Evidence.

Tips for Executives – How to Get the Data You Need

One of the most common complaints that we hear from leaders and executives is that they have “too much data” and “not enough information”. Some examples of what they mean by “too much data” include:

Reports that consist of pages and pages of numbers
Tables of figures with no overall summary number
Charts that are cluttered and confusing
Analyses that show a lot of numbers but no “so what” message

It doesn’t have to be that way. Here are a few tips that executives can use to get the data they need:

Tip 1: Ask yourself “What information would help me be more effective?”
It may sound selfish, but you should ask yourself “What information would help me be more effective in my job?” This might be information that helps you save your own time, make better decisions, or seize big opportunities.

Another way to approach this question is to review the data that you already have available and ask yourself “What isn’t this telling me?” or “Why is this not useful to me?”

Based on this thought process, prepare a simple table with two columns. In the first column include a description of what you want, and in the second column identify why you want it. Then choose your top 3 to 5 items on the list. Now you’re ready to start the next step – following up with your Data Team and/or your Business Intelligence people to have a first conversation about your top-ranked items.

Tip 2: When people say data isn’t available, use the “5 Whys”
Many data people have difficultly seeing the world beyond the standard data that they use every day. So, when you meet with them and tell them about the data that you need, chances are that they will reply by saying “that just isn’t available”.

When it comes to data – almost anything is available – it’s just a matter of how much you’re willing to fight to get what you need.

The “5 Whys” is a simple process of getting to the root of an issue. When your data people tell you that getting the data you need is impossible, ask “why”. They will give you a list of reasons such as “it’s not in the data warehouse”, or “we don’t measure that”, or “the system doesn’t allow that type of reporting”. Pick any of the reasons, and then ask “why” again, which will generate a new list of reasons. Continue this until you’ve reached the root of the issue (hopefully in 5 or less “whys”). The root issue is often one or more of the following:

Nobody thought to ask for this before
At some point in the past, somebody decided that it was too hard to collect the data
The people running the analysis and reporting are limiting themselves based on the capabilities of their reporting tools
Nobody has thought of taking a prospective data collection approach, and/or nobody has thought of doing a sampling approach (to reduce data collection costs)

Through a few meetings, you now should have the real reasons why you don’t currently have the information you need. You may even have a sense of how much it would cost.

Tip 3: Estimate the cost of not having the information you need
The last step is where you can make your convincing argument. For each of your top-ranked ideas, you can think about what it’s costing you to not have access to that information.

Does it translate to productivity? Lost time? Missed opportunities? Lost revenue? Customer loyalty? Employee turn-over? If so, then you can translate these consequences into real tangible costs. This isn’t an exercise of doing high-precision activity based costing – instead this is just getting the cost estimates roughly right.

These figures give you an idea of how much your organization could potentially invest into better data and reporting. If you’re business-minded and you could work out the actual investment amounts that would still generate a positive return on investment.

Armed with this analysis, now you’re in a position to convince others what this information is worth. Which brings us to our last step.

Tip 4: Gain the support of the leadership team
Chances are that the information that will help you be more effective in your role, will also be useful to others in the leadership team and throughout your organization. If you can gain the support of the rest of the leadership team then you can increase the chances of getting what you want.

Each team dynamic is different, but a one-on-one approach often works well. These can be quick conversations with each leader with a real focus on “what’s in it for them”. You may be surprised with how many of your peers are equally frustrated by the lack of good information.

With the support of the team, the cost of not having the information and some return on investment estimates, you’ll be able to drive to get the information you need to be successful.

These are just a few tips, but I’m sure there are many of leaders out there who have many more great ideas and experiences. If you have suggestions, or alternate points of view, please weigh in.

Note: What is a Data Team?
When we refer to “Data Teams” it’s a catch all for groups of technical, statistical, and subject-matter domain experts that are involved in providing information to support their organization. These teams are sometimes called “Business Intelligence”, “Decision Support”, or “Information Management”, but they can also be internal consultants such as “Operations Analysts”, “Strategic Information” or “Research”. Many of these concepts equally apply to teams of Data Scientists.

Tips for Data Teams – The Consistency Check

Have you ever delivered an analysis, only to hear from your client that “these numbers can’t be right”? It’s hard to convince someone that your results are credible when they don’t even pass the first 5 seconds of review. As much as we may not want to admit it, sometimes the numbers are indeed wrong, so how do we avoid these situations from happening? One type of check that a Data Team can adopt is the “Consistency Check”. Here are some questions that you can ask yourself when doing a consistency check:

Question 1) Are the numbers consistent with themselves?
When building complicated analyses, different sections of the analysis can fall “out of sync” with each other if they are not all updated in the same way. When this happens it can produce inconsistent summary results (i.e. the cover page reports 255 conversions per hour, but the supporting details on other pages show 237 conversions per hour). Sometimes we place too much faith on our reporting tools and assume that they will report exactly as intended. In other situations it’s just a matter of being too close to the work. After a while the numbers are burned into your short term memory and you lose your ability to critically review them with an objective eye. Suggested work-arounds include:

Have another member of your team do a consistency check on the results, preferably someone who hasn’t been involved in the work.
Take an old school approach. Print out the results, and use different colored highlighters for each type of metric. Highlight the summary numbers that represent the same result, and confirm that they are indeed consistent. Continue until you’ve highlighted all summary numbers.
Take another old school approach. Get your calculator out or use a separate spreadsheet, and confirm that you can replicate the summary numbers just based on the results that are being presented. You may be surprised with how many of your clients are doing this with your results already.

Question 2) Are the numbers consistent with your previous analyses?
When a client receives a new set of results they often pull up the previous results that you gave them. They are asking the question “how much have things changed?” You can beat them to the punch by doing this consistency check yourself. To be more specific:

Start with the previous result that was presented or released. Compare the summary numbers from the previous results to your current summary numbers.
Assess if the changes are interpretable. If they are, then this interpretation will likely be part of what you communicate when you release the new results. If the changes are not interpretable, then it’s time to go back into your current results, or your previous results to diagnose why the changes aren’t explainable.

Question 3) Are the numbers consistent with other reports?
Stepping into the shoes of your audience, you can think about the other reports that they are referring to on an on-going basis. It doesn’t matter if the other reports that they use came from a completely different source – from their perspective all data from all sources is supposed to tell the same story. In a similar manner to Question 2, you can do some additional homework so that your results are valuable to your audience as possible. For example you could:

Ask your clients if they have any other reports that they use frequently, and if they would be willing to share them with you. You can frame it honestly – you want to make sure that your results are valid, and if they are different from other sources, you want to be able to explain why.
Do a little research on your own, in particular, reviewing any routine corporate reporting, or industry reporting. Sometimes, a skeptic can be won over by proving that you did your homework. Again if the numbers line up from other sources, it becomes something you can report as proof of consistency. If the numbers don’t line up and you can’t explain the difference, then it may be an indication that you need to review your analysis.

Question 4) Are you telling the right story?
Taking all of the above into account, you should be able to deliver your results confidently. You should now know that the numbers in the report are consistent amongst themselves, that the analysis is consistent with previous analyses, and that the results are interpretable in comparison to other sources. This now can become part of your summary and presentation of your stunning new work. Or at least it can form as an addendum to the email, or the presentation that shows your audience the efforts that you went through to ensure that the numbers are the right numbers. Then you have the foundation to begin telling the actual story of the analysis (the “so what” message).

These are just a few tips, but I’m sure there are many of experts out there who have many more great ideas. If you have suggestions, or alternate points of view, please weigh in.

Analytics Advantage

Helping leaders make the most out of their organization using Analytics

Tag Archives for information for executives