Today’s “A:360” podcast answers the question “How do you know if you have the right KPIs?”. I discuss three quick ways you can tell if you have the right KPIs, and what key points you should consider surround your key performance indicators.

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Hey everyone. Welcome to today’s A:360. My name is Brewster Knowlton, and today we’re going to be talking about how you can tell whether or not you have the right KPIs at your organization.

There’s a famous quote by W. Edwards Deming that says, “You can’t manage what you don’t measure.” The managers of your organization will naturally manage towards driving success for what they are measured by. If our key performance indicators align with what we want our managers to actually accomplish, then, naturally, as a byproduct of having the correct KPIs, our managers will improve performance and manage towards achieving those objectives and thresholds set forth by our KPIs and key metrics.

The question therein lies, how do you know if you have the right key performance indicators? We’re going to quickly talk about three key ways that can help you figure out whether or not you’re tracking the right KPIs and the right key measures.

One of the first ways you can tell if you’re tracking the right KPIs is that you have less than 10 KPIs (and, really, five to seven is your best target). But, if you have more than 10 KPIs, you’re probably tracking too many tactical things and not enough strategic things. Or, you’re trying to tackle too many strategic objectives at once (another bad thing). A quick way to tell if you’re tracking the right KPIs is [answering the question], “Do I have between five and seven? Or at least less than 10?” That tells me that you’re tracking only the most strategic things and that you’re focusing on the most important measures that you need to be tracking.

The next way you can tell you have the right KPIs is that your key performance indicators align with your strategic goals. I alluded to this in the last point, but your KPIs must directly support the strategic objectives of the organization whether they be for the next year or the next three to five years. What are those key objectives? And then, ask yourself if your KPIs support those key objectives.

If you think back to Deming’s quote, “You can’t manage what you don’t measure”, it emphasizes the importance of KPIs measuring progress towards strategic goals, because then, naturally, our teams will manage towards success and achieving those strategic goals. It is, therefore, critically important that your key performance indicators align with your strategic goals.

One of the last ways you can tell that you’re measuring the right KPIs is that your KPIs are different than everyone else’s. That’s not to say that there’s not going to be some overlap, but your KPIs need to be unique to your organization and the specific strategic objectives that your organization has.

Benchmarking is a critical aspect of a lot of peer comparisons in many industries, but that doesn’t mean that those benchmarking measures should be your KPIs. Credit Union A, for example, might have high deposits and, therefore, are more interested in loan growth. Credit Union B, however, might have high loans and would be more interested in deposit growth. You have to assess what objectives your organization has and only your organization. Focus not on what your competitors track, but what is best for your organization. Once you determine those strategic objectives, identify those five to seven key performance indicators that will monitor and track your progress towards success in those objectives.

Wrapping up, you can tell if you have the right KPIs by these three points:

  • You have less than 10 KPIs. Ideally we want between five and seven.
  • Second, your KPIs align with your strategic goals.
  • Third, your KPIs aren’t the same as everyone else’s. They accomplish the unique objectives and help you track your progress towards your unique strategic objectives only.

That’s it for today! Thanks again for listening to today’s A:360.

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Today’s “A:360” podcast answers the question “What is an enterprise data strategy?”. I describe what an enterprise data strategy is, what it must include, and some of the key points it needs to address. A well-defined enterprise data strategy will become the foundation of your analytics success and is of critical importance in your efforts to become a data-driven organization.

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Hey everyone! Welcome to today’s A:360. My name is Brewster Knowlton, and today we’re going to be answering the question: “what is an enterprise data strategy?”

There’s a great website, Dataconomy, that has a definition of an enterprise data strategy that I love. They define it as:

A comprehensive and actionable foundation for an organization’s ability to harness and leverage data.

There are a couple of words in that definition that stick out to me.

The first is comprehensive. If we’re talking about building a strategy for the entire organization – an enterprise data strategy – we can’t just focus on one or two areas. We can’t just focus [only] on lending and marketing. Or operations alone, or marketing and operations. We have to focus on the entire organization.

The data strategy has to be comprehensive.

The other important word in that definition is actionable. The data strategy has to be actionable. It can’t just be a “binder-on-the-shelf” type of strategy where it’s a big book, there’s a lot of words, there’s a lot of pages but it’s not really saying much.

A data strategy has to be actionable. It has to address the technical aspects. It has to address the cultural aspects. It has to tell you how you’re going to go from point A (where you are today) to point B (where you want to be as a data-driven organization). Not just in theory, but in practice. You have this strategy, but how do you execute on that strategy?

Having an actionable foundation built into a data strategy is absolutely critical.

Building an enterprise data strategy, a data strategy that’s going to help you evolve from a not particularly data-driven organization to one that is can be a pretty intimidating task. Here are a few things to start with that might get the ball rolling.

First, compile a data inventory. Go through all of the different applications you have, all of the different databases, and start asking people if they have access databases or important Excel spreadsheets that they’re maintaining on their own computers. Start by getting an inventory of all those data sources that are out there.

After you build this data inventory, I’d recommend building a report inventory. This is very similar to the data inventory except instead of identifying all the data sources, you want to identify all of the reports – specifically, the recurring [reports].

This is important because it helps you identify where the majority of your reporting efforts are being focused within the organization. It also tells you what type of return you might be able to get early on by automating some of these reports. ROI and analytics can be a tricky thing to calculate sometimes. However, for organizations that are just starting their data and analytics journey, automating recurring reports and manual processes that take a lot of time are great ways to generate FTE savings up front and start to show the value of analytics.

There are quite a few other key concepts that have to go into your data strategy, most of which we will talk about in later podcasts. But, the last thing I want to talk about today is the importance of developing a data dictionary. I’ve addressed this a number of times in my previous podcasts and my repetition just emphasizes the importance of it.

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You have to define key terms like member, service, product, household, or any other key term that’s critical to your business and for the analytics that might be generated

You’re already analyzing your organization’s data needs, wants, challenges, and getting a picture of where you are and where you want to be. Building in some definitions and recommendations for those definitions is advised because it starts to get people to understand the importance and the impact that their decision-making on these key definitions will have going forward.

In summary, an enterprise data strategy is, and I’ll repeat this definition from earlier, a “comprehensive and actionable foundation for an organization’s ability to harness and leverage data.” It has to be comprehensive in that it looks at the entire organization, not just one or two specific departments. And, it has to be actionable. Instead of just addressing these high level conceptual or theoretical principles of data and analytics, it has to address the technical aspects. It has to address the cultural impacts, which we’re going to talk about later on. It has to address KPIs, which we have discussed before, and actually, in our next podcast, we’re going to talk about ensuring you have the right KPIs. We brought up data governance and talked about key definitions.

An enterprise data strategy should become your blueprint for going from where you are today as an organization with data to becoming a data-driven organization.

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Today’s “A:360” podcast answers the question “What is a data warehouse?”. Learn what makes a data warehouse different from a “regular database” and what one could do for your organization.

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Hey everyone. Welcome to today’s A:360. My name is Brewster Knowlton, and today we’re going to be answering the question: “What is a data warehouse?”

For starters, a data warehouse is just a special type of database. It has a few unique features in the way that it’s designed and setup that make it really valuable to us for the purposes of business intelligence and analytics.

The first major difference is that a data warehouse integrates data from multiple data sources. Let’s say you have a core, or a loan origination system, or a CRM system – for each of those applications there is going to be a separate database. And those databases don’t communicate with each other unless you’ve done some advanced integrations or custom development.

A data warehouse takes all of the data from all those different applications and integrates it into one, central data repository. [It is] one central location, a single source of truth if you will, that houses information about, in the case of a credit union, all of your members, products and services they have, their online banking interactions, their non-monetary and monetary interactions from the CRM system, their debit and credit card data… all of these different, currently disparate applications. A data warehouse takes all of that data and brings it into one central place where you can get that 360-degree view of your membership.

Another key feature of a data warehouse is that it is designed to read data out of it as opposed to write data into it. What this means is that is it optimized for you to retrieve results, retrieve datasets out of this database, as opposed to writing data to it. In a data warehouse, you’re only updating or adding new information every night for the most part. Whereas an operational database – like one that would sit behind a CRM system or any other on premise technology that has to be written to frequently – has to be designed and optimized for write-based operations.

So, for you to be able to run complex queries in a data warehouse – because those operational databases are designed to write instead of read data – some of your queries that you’re going to run are going to take a pretty good amount of time to load. And if you have business users that you’re trying to deploy reports to, they’re not going to be too happy if they have to wait 2, 3, or 4 minutes for a report to render.

Though this point is a little bit more technical in nature, the fact that a data warehouse is designed to read data and retrieve results very quickly (as opposed to writing them because that only happens once a night) is a pretty important feature especially as you’re trying to deploy analytics and reports throughout the organization.

The next important feature of a data warehouse is that it is designed for historical reporting. It’s designed to, instead of just track what a balance is today, you’ll be able to ask questions like, “What was it today? What was it yesterday? Last month? Last year? Two years ago?” And so on. You have this historical analysis. And as we start to go into a world where we want to ask questions like, “What will happen next month?” As opposed to, “What happened last month?” We need to be able to have this historical information for the purposes of trending, for the purposes of predictive analytics, and a lot of the more advanced features that come as a byproduct of having this analytics platform built from a data warehouse.

The last feature of a data warehouse that I’m going to talk about, at least for this podcast, is that it can enforce the consistency of data definitions. I alluded to this point in the previous podcast talking about what it means for an organization to be data-driven.

It is incredibly important to have consistent definitions for key terms like member, product or service. A data warehouse can do a really good job of enforcing those definitions by having those definitions already built in, so, when users pull reports – like a current member report – it will provide the same information whether person A from department A pulls it, or person B from department C pulls it. A data warehouse does a really nice job of enforcing those data definitions that we want to have be consistent. This is a critical component of being a data-driven organization.

I could spend 20 or 30 minutes going on talking about what makes a data warehouse unique and different from a regular database. But the four key points I want you to walk away with are this:

  • A data warehouse integrates data from multiple data sources
  • A data warehouse is designed for read operations as opposed to write operations making it faster and more efficient for reporting and analytics
  • It aggregates historical data and captures historical data so that we can do trending analysis and other historical analysis whereas operational databases have more current information and less historical
  • A data warehouse enforces and ensures consistency of important data definitions
    • Terms like member, product, service, or household. Tt helps enforce those key terms that are really critical to having a strong analytics foundation.

Thanks for tuning in to today’s A:360!

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Data is everywhere. Some of us are using data better than others, yet there is no denying that we all have vast amounts of data at our disposal.

But remember, data is not information. Raw data must be turned into actionable information for it to have any real meaning. This is why we emphasize the importance of Key Performance Indicators (KPIs). KPIs are the quantifiable measures that a company uses to gauge its strategic progress. These key performance indicators help us turn raw data into actionable, useful pieces of information.

Are You Tracking the Right KPIs?

Clearly, key performance indicators are an important aspect of the strategic focus on data. Yet many organizations do not track the right KPIs. The “right” key performance indicators must align with their strategic and operational goals. If a credit union’s goal is to achieve deposit growth, then some aspect of deposit growth must be part of that organization’s set of key performance indicators.

You can’t manage what you don’t measure. – W. Edwards Deming

We tend to manage towards what we are measured by. If we define the proper KPIs, then managers will manage towards the goals we wish to achieve simply as a byproduct of how they are being measured. By aligning management with the proper KPIs, the organization will naturally align with strategic goals and progress towards these goals as defined by the key performance indicators.

Three Ways to Know You Are Tracking the Right KPIs

1. You have less than 10 KPIs.

The premise here is that if you have too many key performance indicators, you really don’t know what your strategic goals are (or you simply have too many). Even 10 is too many KPIs for most organizations! I typically suggest that an organization focus on 5-7 key performance indicators. Remember, there may be a large number of operational metrics that support those key performance indicators, but we only want to focus at the strategic level on the most important 5-7.

2. Your KPIs align with your strategic goals.

Really ask yourself what the strategic goals for the organization are over the next three-to-five years. Once you have those goals written down and agreed upon, ask yourself “do our key performance indicators truly measure our progress towards the strategic goals?”.

If the key performance indicators do not directly support the strategic objectives of your organization, then the current measures simply are not the correct KPIs.

Repeating Demings’ quote, “You can’t manage what you don’t measure”. By defining key performance indicators that measure progress towards strategic goals, we enable our teams to manage towards our strategic goals.

3. Your KPIs aren’t the same as everyone else’s KPIs.

Key performance indicators are (and should be) unique to your specific business. In the financial industry, we tend to emphasize peer comparisons and benchmarking. This is a valuable exercise and has its merits, but your key performance indicators should not be about benchmarking or about matching up with what others are measuring. KPIs should be specific to your business and your business alone. Obviously, there will be overlap where financial institutions have some of the same key performance indicators. However, do not track a KPI simply because “Credit Union ABC” tracks that measure. What might be a good KPI for “Credit Union ABC” may not fit the strategic goals for “Credit Union XYZ”.

Need help figuring out what KPIs you should be tracking? Fill out the form below, and we will be in contact shortly!

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According to The New York Times, “[Boston’s] City Hall is humming with data.” Drawing on various data sources at their disposal, Boston is leading the way in being a data-driven city through the use of their revolutionary program, CityScore.

CityScore and The Mayor’s Dashboard

According to the City of Boston’s website, “CityScore is an initiative designed to inform the Mayor and city managers about the overall health of the City at a moment’s notice by aggregating key performance metrics into one number.” The tool uses a series of metrics that measure the city’s success compared to performance targets. The city is measuring everything from 311 Call Center Performance to Boston Police Response Time to measuring the rate of homicides, stabbings, and shootings.

Each measure is scored such that a 1 implies that the City is achieving their performance targets, a score greater than 1 indicates that performance is exceeding the target and a score less than 1 indicates that performance is below target.

Boston's CityScore Dashboard on September 13, 2016.

Boston’s CityScore Dashboard on September 13, 2016.

The Mayor’s Dashboards is another way the city is using data and analytics to track key measures. These measures are strategic to the goals of the City of Boston and for Mayor Walsh. “The [CityScore] team uses a set of large dashboards, mounted on the wall facing [Mayor Walsh’s] desk, to visualize what the City is doing to realize these objectives.”.

One page of Boston's Mayor's Dashboard

One page of Boston’s Mayor’s Dashboard

CityScore’s Impact

CityScore was implemented in January of 2015. In its short time in production, there have already been some interesting insights provided by the dashboard. According to the City of Boston’s website, EMS Median Priority 1 Response Time was 5 minutes and 59 seconds in January of 2015. By April of 2015, that number had jumped to 6 minutes and 11 seconds. This led to an inquiry to determine the root cause of the spike in EMS response time.

Driven by the analytics of the CityScore dashboard, it was discovered that though Boston’s population had been increasing, EMS funding had remained unchanged. Without the ability to hire more first responders coupled with more emergency calls as a result of a larger population, the City was able to prioritize EMS funding in the next budget cycle. According to the CityScore website, the City new budget enabled “EMS to train a new class of 20 EMTs and buy 10 replacement ambulances”.

While it certainly will evolve with time, CityScore is an impressive use of analytics in a space that has largely been lacking in its data-driven competencies. The City of Boston has provided the CityScore Toolkit that other cities could use for their own analytics.

We hope this trend continues as more and more industries and organizations embrace the use of analytics and become data-driven!

If you are a doubter on the power of leveraging data and analytics, you belong to an increasingly small group of non-believers. While we are all starting to agree on the power of analytics, actually implementing a data and analytics program is more challenging than it may seem. From the importance of having a clearly defined data strategy to taking the right steps to develop a data-driven culture, they are many nuances to getting started with analytics.

One of the best suggestions I have when starting an analytics program is to identify quick wins. Identifying these “low hanging fruit” is a highly recommended way to build some early momentum. For those of you familiar with Jim Collins’ book Good to Great, you may remember his discussion of “The Flywheel Effect”. In this post, I’ll quickly highlight how “The Flywheel Effect” applies to analytics and how some early wins can create long-term success.

What is “The Flywheel Effect”

Below is Jim Collins’ description of “The Flywheel Effect”:

Now picture a huge, heavy flywheel. It’s a massive, metal disk mounted horizontally on an axle. It’s about 100 feet in diameter, 10 feet thick, and it weighs about 25 tons. That flywheel is your company. Your job is to get that flywheel to move as fast as possible, because momentum—mass times velocity—is what will generate superior economic results over time.

Right now, the flywheel is at a standstill. To get it moving, you make a tremendous effort. You push with all your might, and finally you get the flywheel to inch forward. After two or three days of sustained effort, you get the flywheel to complete one entire turn. You keep pushing, and the flywheel begins to move a bit faster. It takes a lot of work, but at last the flywheel makes a second rotation. You keep pushing steadily. It makes three turns, four turns, five, six. With each turn, it moves faster, and then—at some point, you can’’t say exactly when—you break through. The momentum of the heavy wheel kicks in your favor. It spins faster and faster, with its own weight propelling it. You aren’t pushing any harder, but the flywheel is accelerating, its momentum building, its speed increasing. – Jim Collins from his blog

Like a flywheel, starting an analytics program from scratch can be hard work. Like trying to go uphill on a bike in sixth gear, it can seem like no matter how hard you pedal you aren’t going anywhere. But, once you start to get the wheels turning, it becomes easier to go faster and faster and faster. Eventually, it becomes so easy to keep momentum that you feel as if the bike is pedaling itself! This is Jim Collins’ “Flywheel Effect”.

Quick, Early Wins are Essential

Starting a data program certainly has the feel of Collins’ flywheel. In our world, getting some early, quick and (ideally) easy wins is a great way to start turning the wheel. For organizations with a relatively low business intelligence maturity, quick wins are plentiful. Identifying and successfully achieving those early wins is crucial to ensure buy-in and begin to foster a data-driven culture.

How Can I Identify the “Low Hanging Fruit”?

The next logical question, of course, is how do I identify these quick wins? In my experiences, the easiest way to identify a quick win is to simply ask analysts and operational staff what types of reports they produce on a recurring basis. When performed organizationally, I call this a “report inventory”. The average $1 billion financial institution I work with has at least 5,000 hours that could be saved by automating recurring reporting processes. This adds up to hundreds of thousands of dollars in FTE time cost that could be reduced. Imagine how much an employee would be raving about your BI team if you could give them half of their week back! (And imagine what that employee could do with the ability to be that much more productive and efficient?!).

These reports can be automated using tools like SSIS, SSRS, or other visualization and BI tools your organization may own (like InformationBuilders, Tableau, etc.). Not only will these quick wins start to build momentum for your analytics program (turning the flywheel), you will also make some colleagues of yours quite happy!


Need help getting started with analytics? Reach out to us and ask how we can help!

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For starters, yes the title is a terrible play on “when life gives you lemons, make lemonade”.

Bad jokes aside, I hear too frequently how organizations need more and more data. I’m a data guy – I’m all about data. But there is a subtle difference between having more data and more information.

Below is one of my favorite quotes about data:

Without data, you’re just another person with an opinion. – W. Edwards Deming

Opinions are the foundation of subjectivity, and subjectivity fundamentally is devoid of data as support. Decisions driven by opinions without data is counter to everything The Knowlton Group (and I personally) stand for. Our primary mission is to enable every organization to become data-driven.

But as much as I like Deming’s quote, I also love this quote from a 2015 Forbes article:

“Without an opinion, you’re just another person with data” – Milo Jones and Philippe Silberzahn

The converse of Deming’s quote is equally accurate. Having data without an opinion or interpretation of that data is as bad as forming opinions without any data to back you up. With all of this data out there, it is foolish to believe that data will tell us what we need to do. All data requires interpretation and opinions to be formed before it can be practically applied.

The Proper Way to Use Data

Though this section is a gross oversimplification, it boils down the proper way to use data into a few simple steps. Following this process will enable you to turn data into information.

1. Ask a Question

Every actionable use of data must start with a question. These questions can be relatively simple like “how many members do we have” – unless there is no consistent definition for a member! .

The questions you ask can also be more complicated like “do we need a new branch?”. Regardless of what the question is, properly leveraging data and analytics requires asking a question to which you hope to discover an answer.

2. Form a Hypothesis

Like the null hypothesis in statistics, I strongly believe that you must start with a hypothesis. This is where your opinion and subjectivity can come into play. Use this hypothesis as a method to which you test your analysis against. Be sure that confirmation bias doesn’t play into your analysis. If your hypothesis turns out to be incorrect, who cares!?.

“I have not failed. I’ve just found 10,000 ways that won’t work” – Thomas Edison

3. Test your Hypothesis

With a question asked and a hypothesis formed, now you can begin to discover an answer to your question and test your hypothesis. This is where we can start to dig into the data (and the fun really begins). Simpler questions may require you to gather data from a single source. More complicated questions can require analyzing data from multiple data sources. For the more complicated questions, a data warehouse really starts to prove its worth!

Avoid gathering more data than you need and overcomplicating the process. Paralysis by analysis is a real thing. Gather and analyze only the data you need to test your hypothesis. Here are some of my favorite quotes/sayings on avoiding unnecessary complexity to motivate you:

“KISS – Keep it Simple Stupid” – Kelly Johnson, Late Lead Engineer for Lockheed Skunk Works

“Simplicity is the ultimate sophistication” – Leonardo Da Vinci

“Make simple tasks simple!” – Bjarne Stroustrup

4. Analyze Findings and Provide an Explanation

This is one of the most overlooked steps of the process. Asking the right question of your data is crucial, but you must provide an explanation or recommendation based on your analysis. Too often I see fancy documents put together with many pages and loads of charts and spreadsheets. In these instances, you’ve written a lot but said little.

Be concise, be clear, and provide an answer (for simple business questions) or a recommendation (for more complicated business questions).

Wrapping Up

Properly using data and analytics is becoming invaluable and, ultimately, necessary for most organizations. To begin developing a data-driven culture, have your staff read this article to learn how to properly turn data into information.

There is A LOT of data out there. The most successful companies (and people) know how to turn that data into information.


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We have previously discussed how becoming data-driven is essential for organizations in today’s digital world. In fact, the Society for Industrial Organizational Psychology listed maximizing data and applying analytics first in its list of the top 10 workplace trends of 2016. While learning how to maximize data and analytics to improve efficiency and make better business decisions is crucial for businesses to remain competitive, it is just as vital to properly acclimate and prepare employees for some of these changes.

In order for employees to optimize a data-driven environment, be sure to consider the following:

Give as much notice as possible

Any type of change, whether it be new technology or new processes, can be very intimidating. Giving too short notice or no notice at all can cause stress and anxiety for employees. The more notice you give to employees the more they can prepare themselves for whatever new implementation will be taking place. This will also be less disruptive and yield less pushback from individuals affected by the new changes being implemented.

Be transparent

Similar to providing notice, transparency helps for an easier transition when preparing employees for a data-driven culture. Employees are less likely to feel stressed or resentful towards management and the new process or technology. Being transparent also fosters trust. Individuals will be more open to new procedures when they feel they can trust the process and those implementing them.

Provide “The Why”

Humans are creatures of habit and are often resistant to change. Even if management knows new technology will make employee lives much easier and more efficient, employees may not see it that way. They may feel inconvenienced, intimidated, and frustrated. Explain to your staff why the change is being implemented and how it will improve the organization. Get people excited and interested to take on new challenges. The goal is to provide meaning for employees so they’ll be more likely to support whatever will be implemented.

Assess the organization’s data-driven readiness

It’s crucial to be aware of the state of readiness of your audience. For example, if employees are going to be using a new software, it’s safe to say there would be little to no knowledge abut this product. As a result, plan for that. Completely new technologies or processes require a more thorough preparation than small adjustments. Don’t just throw something new out to your employees and assume they’ll figure it out. Take the time to understand what people know and what they don’t know, then plan accordingly. Would you ever implement a new core without training your staff? Of course not. The same principle applies to your data and analytics projects.

Train and prepare

The level and length of training will depend on the readiness of those being affected by a more data-driven culture. For a small implementation, holding a short meeting for Q&A may be sufficient. For larger overhauls, formal training and guides are typically required. Regardless of the extent of the impact of change, providing as much information and background as possible is ideal.

Provide a support system

Like most groups, there are those who learn quicker or find new technology more intuitive than others. Similarly, there are those who struggle with change or aren’t quickly able to embrace new technology and processes. Try to find ways to create a support system in order to ensure a smooth transition and to avoid resistance. Providing mentorships or open-door policies for questions and concerns are ways to help make changes in the workplace easier and more effective. For systematic issues that a larger number of employees share, consider focus groups to understand the root of the problem.

Reinforcement is essential

Letting people know they’re doing well or that the new culture is providing a positive impact will go far with your employees. Reinforcement provides reassurance and lets people know they’re doing a good job. This helps to foster a unified environment and keeps people motivated and confident during times of change.

If you can apply these techniques during your data and analytics implementations, we have no doubt you can achieve greater buy-in and utilization amongst your staff. Like any other technology initiative or major process change, properly readying staff and supporting them throughout the effort is critical to success.

Not getting the most out of your data and analytics projects? Reach out to us and ask how we can help!

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In our previous post, we introduced you to the data and analytics management methodology, “centrally driven, broadly distributed”. Having discussed the reasons why a “centrally driven” approach to data and analytics is optimal, this post will dive into the second half of the quote, “broadly distributed”.

“Broadly Distributed” Analytics Takes Down Silos

In many organizations without a robust data and analytics program, analytics is typically managed within silos as discussed in part 1. The reporting and analysis is then rarely distributed to the rest of the organization. Rather, it is kept “close to the chest” of the department that originated the analysis. This creates a “broadly driven, centrally distributed” approach whereby analytics originates in departmental silos and never leaves those silos.

Organizational transparency is critical to a strong data and analytics program. Marketing can no longer operate in a vacuum without support and communication with IT, Lending or Operations (to name a few in a banking context). These interdepartmental efforts create the need to view reporting and analytics across department reporting lines as well.

A “broadly distributed” approach to data and analytics ensures that data can be consumed by all departments in the organization. Of course, security measures must be ensured for particularly sensitive data, but operationally data transparency is of critical importance. If a new lending promotion is being planned, Lending shouldn’t have to jump through hoops to get Marketing data. Similarly, Marketing shouldn’t have to jump through hoops to get data from the Lending team.

Silos are bad.

From “I” to “We”

In organizations where analytics is not front-of-mind, there lacks an enterprise-wide view of processes, goals and operations. When data and analytics is “broadly distributed”, staff start to see how their department’s efforts contribute to the organization’s strategic goals.

Enterprise wide distribution of reporting and analytics enables inefficient processes to be identified early and reduce their negative impacts. For example, if a new lending product is not interfacing with the core properly, Lending, IT, Marketing, Operations or a staff member of any department could assist in identifying this process flaw.

More often than not, we tend to find that staff are willing and able to adopt an enterprise level view of their efforts. The challenge often lies in giving them the tools and access to fully embrace a view outside of their own department’s efforts.

A “broadly distributed” data and analytics program enables your employees to no longer think only about their department’s operations but about the enterprise’s operations with the big picture in mind.

Accurate Across Reporting Lines

In part 1 of this post, we described a situation where various members of different departments are asked the same business question, “how many members do we have?”. In a “broadly driven” (instead of “centrally driven”) data and analytics model, each staff member would go to their own data sources to retrieve the “correct” answer. Rarely does each staff member produce the same answer.

A “broadly distributed” approach in conjunction with a “centrally driven” model ensures that each employee retrieves data and analytics from the same, single source of truth (add link to data warehouse article). By distributing the “centrally driven” repository of data and analytics throughout the organization, accurate and consistent reporting proliferates throughout the company.

Wrapping Up…

Combined with a “centrally driven” solution, a “broadly distributed” model is unquestionably the optimal approach to data and analytics. Eliminating silos, reducing inconsistencies and inaccuracies and ensuring an organizational mindset are but a few of the positive outcomes of this design. Coupled with the benefits described in our post on a “centrally driven” solution, our hope is that you and your internal team’s see the value in approaching enterprise analytics the proper way.


If your team needs any form of assistance in formulating your data and analytics strategy, give us a call at 860-593-7842 or send an email to brewster@knowlton-group.com!

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In Thomas Davenport’s “Competing on Analytics: The New Science of Winning”, one of the first chapters of the book defines the common attributes of analytically-driven organizations. Davenport discusses that one of the critical aspects to success in analytics revolves around taking an enterprise-level approach to managing data and analytics. He then quotes Harrah’s Entertainment’s management approach to enterprise data and analytics, calling the approach “centrally driven, broadly distributed”.

Focusing on “Centrally Driven”

In this article – part 1 of a two part post – we will discuss the first half of that quote. Having data and analytics be “centrally driven” is of the utmost importance to success and sustainability of your analytics programs.

Inconsistencies Abound

If you are an executive of any industry, I know for certain you have experienced the following scenario: you ask the same data and analytics question to different departments and you get several different responses.

If you are in the banking industry, ask Marketing, Lending, IT, Finance, Commercial, and Retail Operations how many customers/members you have.

How many different answers do you think you will get?

In most organizations, each department would independently retrieve the information that you asked of them. Marketing might go to their MCIF, IT might go to the core, Finance might look at the GL application – rarely will each department go to a centralized, single source of truth to retrieve the “correct” answer. Since each application is managed separately with different data contained within, there is no way to ensure consistency and accuracy without a central data warehouse or repository.

Without a centralized, enterprise-level platform of data and analytics, it is nearly impossible to ensure consistent, accurate reporting.

Spreadsheet Errors

I love Excel as much as the next guy. Quite a few organizations across nearly every industry live and die by their Excel spreadsheets (investment banking first year associates know this all too well). But how many Excel spreadsheets are 100% correct and error-free? Some studies estimate that nearly 9 in 10 Excel files have errors!

Especially in the banking industry, I see organizations littered with complicated spreadsheets. Analysts will extract Excel files from different data sources and then hope they’ve defined their VLOOKUPs accurately to be able to consolidate the data sets. I’ve personally witnessed high-level, executive reports with wildly inaccurate formulas that effectively rendered the spreadsheets useless. This is, sadly, not an anomaly.

How do you resolve this issue? By taking a centrally driven approach to managing data, users will not be required to consolidate data from various sources or manually define complicated, error-prone formulas. Data will be consolidated and validated reducing or eliminating the vast majority of these overly-complex Excel spreadsheets. Data warehouses combined with BI and data visualization tools (like Tableau, InformationBuilders, Qlik, and many others) provide an enterprise-level, industry-leading platform for data and analytics.

Definitions and Data Governance

Whenever I get the opportunity to speak with a credit union, I eventually always ask the same question, “what is your definition of a member? Does everyone have the same definition as you?” This seems similar to the inconsistencies idea brought up at the beginning of the article, but even with the same data source, there is no assurances that everyone will define a key business term the same.

Marketing might only focus on members that have not been placed on a “do not contact” list. What about the same individual (i.e. unique SSN) that has multiple accounts and member/customer numbers? How are they counted for aggregation purposes? These subtleties are critical to a strong data and analytics program. A cross-departmental team must agree on key definitions that are used throughout the organization. This centrally driven approach to defining key business terms helps ensure accountability and consistency.

Wrapping up

A centrally driven approach to data and analytics ensures consistently accurate reporting with key business definitions universally understood by everyone in the organization.

In our second part of this post, we talk about why having a “broadly distributed” data and analytics solution is vital.

Check back soon or subscribe to our blog for updates!

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