Tag: analysisworks

Posted on in Driving Action with Analytics

How to Create KPIs that Clinicians Care About

Healthcare organizations around the world face the challenge of creating Key Performance Indicators (KPIs) that clinicians actually care about. The problem is, it’s usually the administrators who lead these efforts, and they usually use administrative data to drive the metrics and … surprise, the KPIs end up being very administrator-focused.

But that’s rarely the intent. Healthcare leaders often introduce KPIs with a goal of driving improvement through awareness and understanding.

Clinician atlas kpi

So how can an organization create KPIs that clinicians care about? Here are 4 steps:

Step 1: Engage clinicians in the design process
When introducing a change, most would agree that a winning strategy is to involve people who will experience the change in the design process. Clinicians are no exception. They are often very numbers-driven, but want the numbers to mean something to them. The level of consultation and engagement doesn’t have to be a lot … it can be seeking advice from a few key clinical leaders and influencers, or it could be a full-blown focus group with stickies. What’s important is that the clinicians have had the opportunity to give input and feedback.

Generally speaking this is best done with a group of 5 to 10 representative clinicians, led by a facilitator who is good at condensing big ideas and focusing on the biggest issues. It’s also ideal to have a “data person” present, though, the facilitator should take special care not to limit the KPIs to the available data. Try to encourage this group to brainstorm their top 10 KPIs … it’s important to avoid what Brad Hams (author of Ownership Thinking) describes as going to the “KPI Buffet” where people select way too many KPIs to the point that the information is overwhelming.

Step 2: Think about what the clinicians will do differently based on the KPI
After brainstorming the top 10 KPI candidates, the next exercise is to focus on ensuring the KPIs are actionable. If a KPI is actionable it will:

  • Tell you how you are performing on something that you can influence, and
  • Tell you in a time frame that you can do something about it

The “something you can influence” is a key concept. There is no point in monitoring KPIs that people have no control over. In complicated environments like healthcare, where many different people contribute to performance, it’s fairly common for everyone to think the problem is because of someone else. Borrowing again from Brad Hams, you can identify the clinician that has the most influence on a given KPI and then put their name next to the KPI as the KPI owner. This will go a long way towards making at least a few clinicians care about the KPIs!

Ideally, through this lens of making the KPIs actionable, you will have decreased your list from 10 KPI candidates to 5 or less.

Step 3: Keep it to 3 KPIs
As much as we would all like to fix everything right away, the reality is that most people, departments and healthcare organizations can only really focus on a few key areas at one time. So, push the design team to select the 3 best, most actionable, most relevant, and most impactful KPIs. By the time that you’ve charted these KPIs over time relative to their targets, there will still be lots to look at.

Once these first three KPIs are well on target and under management, they can be removed from your top-of-mind monitoring reports and replaced with other actionable KPIs that didn’t make the list.

An alternate possibility is allow 3 KPIs in each clinical area. For example, there are KPIs that are relevant to Surgery (such as post-operative infection rates, or compliance with the surgical checklist) that would not be relevant to Medicine.

Step 4: Put the KPIs into action, and stick with it
There will likely be some work in actually getting the data for these KPIs in a clean, accurate and reliable format. As mentioned before, some of these KPIs might not have data sources yet, and some ground work will need to be done to start collecting the data prospectively. Bear in mind that it’s much more valuable to have manually collected data feeding into an actionable KPI than pre-existing data sources feeding into a KPI that can’t be influenced.

To put the KPIs into action:

  • Post them publicly
  • Build a habit of reviewing them as a routine part of standing clinician meetings
  • Publicly celebrate when targets are met, making sure to recognize each individual who contributed to the success
  • Retire the KPIs when they are under management, and when there are more important KPIs for the top 3 positions. Resist the urge to grow the KPI list.

If you have stories about how you’ve created KPIs that resonated with clinicians, 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


Posted on in Driving Action with Analytics, High Performance Data Teams

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.

Posted on in Driving Action with Analytics

3 Tips for Turning Analysis Into Action

Today’s analytics tools offer deeper insights into your data than ever before. But if you were to take an objective look at your reports, how many of them actually drive action? How often do you spend time looking at your analytics and have the following thoughts:

  • “So, what am I supposed to do with this?”
  • “Ok, our KPIs are up, but how much of it is because of things we did?”
  • “Sometimes we’re above target and sometimes we’re below target … at what point should I start paying attention?”

If you find that you are asking yourself these questions, then you are probably not realizing a return on investment from your analytics. The whole point of having better analytics is to give you, your department and your company the tools to be successful. If your analytics don’t directly support you in taking action, then the entire investment may be wasted.

While organizations are very keen to invest in the latest technologies, they often invest little to no effort in ensuring that the analytics trigger the right actions. This is often the weakest link in the analytics chain.

Weakest Link Analytics
These are some basic tips that your organization can use to turn your analytics into action.

Tip 1: Find the critical few metrics that matter
Many new adopters of analytics are tempted to show all of the data and metrics, assuming that more information means more power. But this approach is often counter-productive, because different people in the organizations focus on different things … instead of everyone pushing towards a common goal.

In Mastering the Rockefeller Habits Verne Harnish describes the top metrics as “the critical few” … and he means it. He encourages organizations to find their one, two or three metrics that show whether things are going well or not. For organizations with a wide suite of performance metrics, some consideration could be given to building an aggregate measure, just like a GPA score for a student. More tips to improve your metrics are described in my post on Escaping KPI Hell.

Tip 2: Assign responsibility to each metric … take names
The expression “when everyone is in charge, nobody is in charge” rings true here. When metrics are reported, and it’s unclear who is responsible for taking action, more often than not people will assume that someone else is taking care of it.

The simple practice of designating individual responsibility to each metric can create drastic change in performance. Once the “metric owner” has accepted this responsibility, their name is written next to their metric in the reports. That individual now feels a personal responsibility to make sure the metric looks good, and will start taking action to make it happen.

There will be situations where the metric owner doesn’t have all of the authority and resources to drive the metric, but they can at least report on the factors that are most affecting performance.

Tip 3: Walk the talk … keep score and show that it counts
Another common reason why companies don’t realize a return on investment on their analytics is that they don’t build in their metrics into their daily, weekly, monthly and quarterly dialogue.

By focusing on a few critical measures (and making sure they are the right ones), leaders should see the review of the analytics as something that helps them, rather than another new task to squeeze into the day. During these reviews the main questions are:

  • How are we doing?
  • Why?
  • Who needs to do what to achieve our performance targets?

This isn’t about finger-pointing … it’s about having a common score card, with specific people in charge of the few key metrics, and empowering them to take the right actions that help the organization as a whole succeed. These very basic tips can go a long way towards increasing the value of your analytics.

If you have stories about how you turned analysis into action, 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



Posted on in Driving Action with Analytics, High Performance Data Teams

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



Posted on in Driving Action with Analytics, High Performance Data Teams

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.

 

Posted on in Driving Action with Analytics

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.
Dropping the hammer
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.

Posted on in Driving Action with Analytics

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.
Big Data and iPad Mega Trends

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.
So what

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”).

Angry data
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.

Posted on in Driving Action with Analytics, High Performance Data Teams

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.

Posted on in Driving Action with Analytics, High Performance Data Teams

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.
 

Posted on in High Performance Data Teams

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.