4 Tips to Unclog Your Data Team

A common problem that good Data Teams face is that they are significantly backlogged. They are pulled in many different directions by different leaders with different priorities. It’s a good sign that they are a valued asset to your organization, but it can be frustrating waiting for them to get to your urgent requests. Sometimes it’s like they are clogged up like bad plumbing …
Unclog your data team

So, what can an organization do to unclog their Data Team? Here are four tips:

Tip 1: Get crystal-clear on the outcomes of the Data Team
Data Teams often spend a lot of time talking about their efforts and the resources they feel they need. But instead it’s better to focus on the outcomes of the team … how will your organization know that the Data Team is doing a good job? Until everyone in the organization (including the Data Team) is clear on the outcomes that they need to achieve, the demands on the Data Team will continue to grow unchecked. Some example Data Team outcomes could be:

  • To ensure top-level management has the reports they need to maintain profitability
  • To provide management insights on market competitiveness
  • To trigger management alerts on operational areas that require attention
  • To detect patterns related to decreasing customer satisfaction
  • To support improvement projects in the organization

… and so on (Hint: It would be unrealistic for most Data Teams to attempt to meet all of these outcomes.)

Once the outcomes of the Data Team are clear, the next step is …

Tip 2: Calculate the ROI for different Data Team efforts
Most Data Teams hold responsibilities for maintaining reports and analyses … some of which are easy and some of which are very hard. Rarely do the users of these deliverables appreciate the effort that goes into them, particularly when there is a lot of interpretation required, or a lot of extra data cleaning that can’t be automated.

In these situations it may make sense to assess if the value of the information is commensurate with the effort involved in generating it. This is especially true if there is a suspicion that the information isn’t really being used for decision-making. More tips are on this topic are described in the blog post Turning Analysis Into Action, but generally speaking the Data Team efforts should be fully aligned with the outcomes of the Data Team.

If the ROI on a difficult analysis isn’t there then …

Tip 3: Give your Data Team permission to purge
Data Teams typically find themselves in situations where they don’t have enough capacity to meet all of the demands imposed on them. And every week requests for new analyses and reports come up.

So, if they are working on difficult things that are clogging them up, empower the team with a business process to periodically review the ROI of the analysis and how popular it is. Set a bar for minimum expectations, and discontinue anything that doesn’t meet it. For example, if a report is only being used by one or two people, that’s a pretty good sign that it could be discontinued. The whole power of reporting is creating common measurement of performance that everyone can get behind. So, if a complex analysis is only interesting to one or two people, then chances are they aren’t aligned with the rest of the organization.

A sure-fire way to test the popularity of a periodic report is to just let the report take a vacation. If you don’t provide the report, does anybody come asking for it? If not, then you’ve just liberated some bandwidth for your Data Team.

But you don’t have to stop there … you can unclog your Data Team even further with the next tip …

Tip 4: Hold some reserve capacity for emergent work
Important and urgent things come up, and when they do, Data Teams often drop everything to respond. So why not maintain some reserve capacity for this? You can even review your past urgent and important requests to get a sense of the timing of these requests … year end, month end, just before planning sessions, etc.

As a Data Team when you plan out your week, and assign responsibilities, try as best as you can to not schedule every last hour. Build a couple hours of flex into every day, or plan for “catch up” days. Worst case scenario, your team members can get ahead on some neglected projects with this flex time. Best case scenario, when your CEO calls needing something urgent, you’ll be able to impress them with your ability to respond quickly.

If you have stories about how you’ve unclogged your Data 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

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.

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.

Analytics DIY vs Outsourcing

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.
Hello I'm the VP of Analytics
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.

Grow your team
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.
Dollar sign
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.

 

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. PS - Dwg - Crawl, walk, run R2

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?

How to get started

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.
 

Positive Psychology and Employees – Data people need recognition too!

The following guest article by Alexa Thompson, discusses how recognizing and encouraging employees’ individual skills and talents – often termed positive psychology – can lead to happier and more productive workplaces. Thompson writes about the connection between happy and productive employees. In our analytical team we’ve learned the importance of recognizing and supporting the individual strengths of each individual. Alexa prepared this article in response to our How to Create a Culture of Evidence post and has authored several pieces for an online psychology education resource.

Until rougly the 1950s, the psychological state was rarely a consideration in the workplace. Managers (even to this day) assume that a reward system of promotions and paychecks would be sufficient to motivate employees. However, the reality of the human psyche has proven far more complex than can be accounted for by the conventional ‘carrot on a stick’ approach.

Positive Psychology

The late Dr. Harry Levinson – a pioneer in workplace psychology studies – argues that a psychological contract exists between employees and employers. When employees feel that their ingenuity and skill set are ignored in the workplace, it can lead to feelings of depression and thus low productivity by disgruntled workers. Research by Levinson and his contemporaries showed that company culture can have a significant impact on worker productivity, loyalty and pride.

Much of the modern thinking on positive psychology can be traced back to 1998, when Martin Seligman, president of the American Psychological Association and professor of Psychology at the University of Pennsylvania, developed master’s program for the study of positive emotion. Over a decade of research since then has found that happiness at work can improve revenue, profitability, staff retention, customer loyalty and workplace safety, as well as increase creativity and problem-solving ability.

Studies of small groups have identified the effects of human resource management. A report by Bloom and Van Reenen for the National Bureau of Economic Research uncovered a number of psychological factors, including security and a sense of fulfillment and connection, that affect an employee’s mindset. “As firms expand in their scope both geographically and in product space, local information will become more costly to transmit so this will […] favor decentralization.” This decentralization allows information to be processed at the level where it is used, lowering the cost of communication as well as increasing productivity through rising job satisfaction. Bloom and Van Reenen state that the “delegation of responsibility goes along with more employee involvement, greater information sharing and a greater participation of lower level staff.” This in turn enhances the quality of work and employees’ alignment with their company’s goals.

Findings in another study for the American Psychological Association further corroborate the importance of positive psychology. In the report, the authors conclude that “well-being in the workplace is, in part, a function of helping employees do what is naturally right for them by freeing them to do so… – through behaviors that influence employee engagement and therefore increase the frequency of positive emotions”. In other words, an environment of altruism and goodwill is often instrumental in creating a healthy, productive workplace culture.

The data and thinking on the subject matter continue to evolve, as it has been for the last six decades, ever since positive workplace psychology has been studied in earnest. Nevertheless, one major theme has emerged and remained clear: paying attention to the individuality of each employee will create a more positive environment for the employee and be of great benefit to the employer, even if there is an initial investment that needs to be made.

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:

Consistent numbers

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.

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.