The Financial Applications for AI and Machine Learning

If you’ve been following our FIRST and SECOND installments of this three-part series on machine learning and artificial intelligence, then you are ready for our conclusion of how these advanced technologies and applications are changing the financial services ecosystem.

Artificial intelligence (AI) and machine learning (ML) have been major contributors to the banking industry well before the advent of mobile banking apps and chatbots. Both AI and ML are beginning to play an integral role in how banks and credit unions operate and interact with consumers: from approving loans to managing assets and protecting against fraud.

Though often perceived as the same concept and used interchangeably, they are quite different. In a nutshell, artificial intelligence is the simulation of human intelligence processes by machines. Machine learning, on the other hand, is basically a sub-field in the larger AI research landscape. It is the process of using algorithms to learn from data and then make predictions about similar data in the future.

Leveraging AI and ML Advantages

With the volume, variety, and velocity of data increasing rapidly, financial institutions are starting to rely on AI and ML to make the most out of the wealth of data they own for more accurate, data-backed business decisions.  Through machine learning, AI can easily and effectively consume and process large amounts of data at an expedited level. Its real-time speed offers efficiency as it continues to learn and become even more efficient over time.

Consider these key applications of AI and ML, and the benefits they offer:

Lower Costs:
AI and ML give credit unions and banks the power to bring together insights from diverse data sets to personalize marketing messages and to offer the right product to the right customer through the right channel at the right time.   ML helps with better marketing efficiency, better use of resources, automates processes and reduces wasteful onboarding promotions with better-targeted marketing campaigns—saving time and money.

Improved Customer Service and Online Experiences:

According to the Content Marketing Institute (CMI), AI can help personalize messages, offers and interactions online. Chatbots use machine learning to understand customer behavior, track spending patterns and tailor recommendations on how to manage finances. Satisfaction is greatly improved when a financial institution can provide product and service recommendations specifically tailored to individual consumers. (1)

Fraud Detection:  AI and ML programs can identify unexpected and suspicious actions for near real-time fraud detection. The value of this in cost alone is significant considering that consumers lost more than $16B to fraud and identity theft last year.  (2)  ML systems have the potential to improve detection of money laundering activity significantly due to their ability to identify complex patterns in the data and transactions.

Better Compliance:  According to Digitally Cognizant, as financial institutions are trying to rein in the cost of regulatory compliance they can unleash AI’s abilities to accelerate throughput up to three times. This could save an estimated 30% of compliance costs annually. Plus, AI is helping regulatory compliance teams interpret regulatory meaning, comprehend what work needs to be done, meet requirements, and codify compliance rules.(3)

Enhanced Revenue and Profitability:  As AI and ML enable financial institutions to better target customers with relevant marketing messages about new products and services, FIs can leverage this ability for more opportunities to onboard new customers/members and cross/upsell existing ones. These refined approaches lead to more profitable business outcomes and growth.

Improved Retention:  AI and machine learning can help identify segments of the customer or member base that are at risk of leaving for a competitor. Through machine learning and predictive algorithms, a financial institution can forecast at what point in a relationship consumers are most likely to leave and what triggers a switch so they can put controls in place to reduce attrition at those trigger points.

Increased Productivity:  The automation of many, once manual, processes that were impacted by human errors is a huge win for financial institutions. By providing continuous automation, ML offers greater speed and efficiency in many areas of operations.  Over the next few years, AI will be used to transform the most central functions in banking, such as inter-bank reconciliations and the quarterly “close” and reporting of earnings, as well as engage in the more strategic functions such as financial analysis, asset allocation and forecasting. (4)

Potential Challenges of ML and AI

Though AI and ML offer countless potential benefits to financial institutions, like with most things, there are potential pitfalls. Some challenges the industry may experience with these innovative technologies include:

  • The uncensored power of smart computers and algorithms presents financial institutions with a source of regulatory, compliance and privacy challenges.
  • Some industry experts suggest that governance of machine learning algorithms is not as strong as it needs to be. According to Federal Reserve, rules such as SR11-7 Guidance on Model Risk Managementdescribe how models should be validated, these rules do not cover machine learning algorithms. (5)
  • The complex IT landscapes burdened by legacy systems pose several challenges when adopting new technologies such as AI and ML.
  • Automation produced by AI and ML will cause an estimated 8% decline in the number of tellers between now and 2024.
  • For ML to succeed, it needs access to large datasets. This means financial institutions must make data freely available to ML software and solutions so they can ingest inputs and churn out accurate insights and predictions.

What the Future Holds

Machine learning and AI will continue to be in the limelight for many forward-thinking financial institution executives—and for a good reason. By advancing forward with AI and ML platforms, credit unions and banks can substantially reduce operational costs and significantly improve the bottom-line.

Whether your institution has already invested in new AI and ML technologies or needing guidance on how to best leverage these applications, The Knowlton Group can help (LINK THE “THE” AS WELL).  Working with banks and credit unions each day across the country, we understand how to help our clients maximize technologies to yield the greatest outcome.   Contact us today to start the discussion.


  1. 8 Ways Intelligent Marketers Use AI; Content Marketing Institute.  August 2017
  2. Fraud Cost $16B to Consumers:  CNBC, February 2017
  3. How Banks Can Use AI to Reduce Regulatory Compliance Burdens: Digitally Cognizant, June 2017
  4. AI Is Becoming a Major Disruptive Force in Many Banking Finance Departments: Forbes, February 2017
  5. Federal Reserve’s Supervisory Guidance on Model Risk Management: Federal Reserve, April 2011

Today’s A:360 talks about how success with analytics can directly support your process improvement initiatives. You’ll hear about how processes can be made significantly more efficient through a deeper analysis into your organization’s application data.

Listen to Brewster Knowlton describe this in more detail in the podcast below or on iTunes!

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Today’s A:360 discusses an idea known as “The Four R’s”. “The Four R’s” is a digital marketing framework The Knowlton Group created to conceptualize analytics-driven digital marketing efforts that the modern consumer expects.

The Four R’s are:

  • Right Time
  • Right Product/Service
  • Right Channel
  • Right Member/Customer

Leveraging a strong data and analytics competency, organizations must deliver the right product/service to the right customer/member through the right channel(s) at the right time.

Listen to Brewster Knowlton describe “The Four R’s” in more detail in the podcast below or on iTunes!

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Today’s A:360 discusses why BI software alone is not enough to accomplish your analytics objectives. BI tools are an integral (and necessary!) part of your analytics strategy. However, they alone will not solve all of your objectives for data and analytics. Learn why this true and how you can properly leverage these technologies.

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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 why BI software alone is not enough for your analytics programs.

As business intelligence and analytics becomes more popular across industries, we’ve seen the rise in the focus of products like Information Builders’ WebFOCUS, Tableau, Qlik and PowerBI. These advanced visualization tools can often make it appear as if you just need to purchase the product and then you’re good to go. In this podcast I really want to dispel that myth.

BI software alone is not enough. You have to have more than just the technology.

You can’t simply purchase a tool to solve your analytics solutions. There are other crucial components to building a successful business intelligence platform, and we’re going to talk about why these visualization tools are not the magical fix in today’s podcast.

Many BI tools are great for visualization, dashboarding, even some of them have ETL or data mapping components to them as well, but alone they’re not enough. Being able to utilize these technologies alone implies that you understand the business processes, that you’ve understood all of the data sources that you need to get at, you have a roadmap and a plan in place, and you have staff that know and are capable of turning data into information. All of these different components that critical aspects of a business intelligence program, those are things that you need in advance or in conjunction with these BI tools. So, these software products alone will not make your BI program successful.

Many of these applications will advertise that you don’t even need a data warehouse to function with them properly, and that these products can capture and model the data themselves. This often assumes that your data is nearly perfect. How many of you can say, “Yes, my data is perfect”? I’d be shocked if anyone listening to this was nodding their head yes to my question (Or isn’t a liar).

So, the challenge with these tools is they can be used in various ad hoc situations to create some data mappings and other important functions, but this is not a true ETL. That is not an enterprise focus. These are one-off situations. Again, there are software and applications that may work for some situations, but we want to encompass a variety of tools and strategies to use for our enterprise analytics platform. Relying solely on BI technology Is not going to solve all of those problems for you.

Now, that’s not to say that these technologies are not vital, because they are absolutely critical to the success of your business intelligence or analytics program. You have to have a technology that is going to transform that raw data into visualizations, into dashboards and make it easily digestible. We’ve mentioned this idea of a central BI portal while talking about the data strategy and what is entailed in those strategies in past podcasts and articles. These BI solutions, these visualization and dashboard technologies, though are crucial to the plan, alone they are not the magic bullet.

These solutions alone are not going to solve your problems and I just want to be very clear that any organization that purchases one of these tools thinking it will be the end all be all, is going to be going down a long road that won’t lead them to the desired or maximum results that they could achieve. Like anything, we need to understand where and when is the best time to apply each technology, and realize that there are many more components to success with business intelligence and analytics than simply having the right visualization software.

In summary, these technologies are fantastic when applied properly and are used as a single component or a critical component of your entire analytics program, but remember, they alone are not going to solve the problems. Your data strategy, your implementation roadmap, all of the other ancillary components of an analytics program are required to be put into place for these technologies to effectively work together and for you to achieve the greatest success.

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

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Today’s A:360 discusses the significance of a data inventory when beginning your data and analytics program. From understanding what data sources and applications your organization possesses to understanding in what format each data source is stored, a data inventory is a critical first step towards building a sustainable analytics program.

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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 inventory?”

A data inventory is exactly what you’d expect it to be. It’s an inventory or a list of all data sources and applications used throughout your organization.

To gather a data inventory, I’d recommend you go through the process of interviewing members of each department throughout your organization. Interview the various staff that are interacting with all of the applications that are driving the business processes – from the C-suite all the way through the organization to all of our front line staff. To gather that data inventory, we’re going to gather all of these people and perform the discovery process which I’ve alluded to a number of times. This is the crux of what makes your data strategy opportunities so successful.

As you’re interviewing these individuals, every time you hear about an application that they use or a system they go to or an Excel file (where maybe they’re keeping track of something) or an access database, that is something you want to add to your data inventory. As you go through the process of these discovery interviews and compiling this data inventory, you’re going to want to capture a few pieces of information about each aspect and each item of the data inventory.

  1. The name of the application
  2. The format that the data resides in. A database? Excel File? Flat files?
  3. Who is the owner of the application? IT? A group or individual within a certain department?

After you identify all of these data sources that comprise the data inventory, you’ll want to go through and identify priority levels for your analytics program. These are the applications that should be integrated first and are of most importance and highest priority. This next group, called the medium priority group, are going to be the more “would like to haves” as opposed to the “need to haves”. Go through that process of prioritizing each data source and this will actually help you build out your roadmap for how you’re going to build your analytics platform.

It’s important to keep track of any changes to the data inventory as programs get de-provisioned, or as you bring new applications into your organization. You’ll want to keep track of these changes. Also, some core systems in the banking and credit union world are changing from a legacy database to a more modern relational database. You want to have that reflected in your data inventory as well so you have the most up-to-date information for each application.

That should give you a pretty good picture of what a data inventory is, what should be included in one and why it’s important.
In the YouTube videos that are attached to this or in the transcription if you head over to our website, you’ll see a sample of what a data inventory could look like, and you could use this for creating your own data inventory as well.

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

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Today’s “A:360” podcast talks about KPIs and answers the question “What is a KPI?”. Learn what makes a KPI different from a standard metric and why they are so critical for your organization’s strategic success. Take a look at our previous posts on KPIs and how to know if you have the right KPIs for further information on this topic!

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Welcome to today’s podcast for our “A:360” series. My name is Brewster Knowlton of The Knowlton Group, and today we’re going to talk about “what is a KPI?”

KPI stands for “Key Performance Indicator”. A good definition of a KPI is: “a set of quantifiable measures that a company uses to gauge or compare performance in terms of meeting their strategic and operational goals.”

The definition of a KPI has two keywords that I want to point out.

The first [key word] is “quantifiable”. If your key performance indicators can’t be quantified, it’s very difficult to gauge your performance towards your strategic goals. A KPI HAS TO BE quantifiable.

The other key word in that definition is “strategic”. We want to look at what are the strategic goals of our organization and then what quantifiable measures support those strategic goals or enable us to gauge our progress towards those strategic goals.

Those are two key words in that definition that I want to point out that are really critical to what makes a key performance indicator different from just a standard metric.

A standard metric, as opposed to a KPI, is going to be a lot more operational or tactical in nature, and lack some of the strategic focus that a KPI will possess. For example, if you’re a CFO for a bank or credit union, your KPI might be “return on average assets” or “efficiency ratio”. Those are pretty important – often times strategic KPIs.

But there are metrics that support those KPIs that may not be as high level or might not be a strategic like, your operating expenses or perhaps your net income. While those are important metrics, they might not be justified as key performance indicators that directly support the strategic goals that you might have as that CFO. So, KPIs tend to be much more strategic in nature, whereas metrics are more operational and tactical.

KPIs are important regardless of whether you’re an organization just starting out with analytics or one who has a much higher analytics maturity. KPIs are critical because they drive your success towards your strategic goals and they help monitor your progress. You really need to emphasize “what are the strategic objectives of my organization”, and once, those objectives are laid out, ask yourself, “What measures can I create that will determine and gauge my progress towards that strategic objective?”

There’s a great quote by W. Edwards Deming that says, “You can’t manage what you don’t measure”. The important part about KPIs is that if you are measuring the right things, then, naturally, our staff will have a tendency to manage towards what we are trying to measure. The byproduct of this is the organization naturally aligns around the strategic goals simply because we are measuring our success through our KPIs that are aligned with those strategic goals.

In summary, KPIs stands for key performance indicators, and they’re the set of quantifiable measures that a company uses to gauge or compare its performance towards their strategic goals.

That’s all for today’s “A:360”!

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In this A:360 podcast, we discuss what it means for an organization to be data-driven. We describe some of the traits and features that a data-driven organization possesses.

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Welcome to today’s A:360 podcast. My name is Brewster Knowlton, and today we’re going to be talking about what does it mean for an organization to be data-driven. The phrase “becoming data-driven” or a “data-driven organization” is being thrown around more and more frequently as organizations start to invest more in data and analytics. But, before this phrase becomes a sort of buzzword, I want to take the time and explain “what does it really mean for an organization to be data-driven” as there are some really valuable aspects that a data-driven organization possesses.

The first trait of a data-driven organization is that the organization uses data – instead of gut instinct – to make decisions. This is a paradigm shift from “I think” to “I know”. Rrom using gut instinct to using information and analytics.

The next trait of a data-driven organization is that they ask “what will happen next month” instead of only asking “what happened last month”? I see this a lot with the banks and credit unions I work with where all of the questions surround [questions like]:

What did we do last month?

What was our loan growth last month?

What was our deposit growth last month? H

ow many members or customers did we add?

But there’s never any thought about what’s going to happen next month. Data-driven organizations try to focus on what will happen as opposed to only looking backwards and saying, “What happened in the past?” There are many instances where looking backwards is important, but there also has to be this idea that we want to look forward and try to figure out what’s going to happen in the future.

Next, data-driven organizations use visualizations. They don’t sit there and stare at overly complicated Excel spreadsheets with these formulas that may or may not even be accurate! What they do is use visualizations through dashboards and other visualization techniques to make complicated information very easily digestible, so with a quick glance, anyone can figure out what’s going on and what the data is telling us.

Another common trait of data-driven organizations is they have all of their data in one, central location. For the listeners of this podcast, that’s – more likely than not – going to be a data warehouse.

As organizations grow, the number of applications they have also tends to grow which makes data more and more disparate. For the credit unions I work with, for example, they have a lot of third-party applications that house valuable member information about their members’ product and service relationship with the credit union. Without having all of that data centralized, it’s very, very difficult to look at a member’s relationship with the credit union holistically and in a 360-degree view way. It’s critical – and it’s a common trait of data-driven organizations – that all of your data is in one, centralized location, more likely than not, a data warehouse.

Data-driven organizations also use data as much as possible to automate business and reporting processes that are manual and recurring or redundant. I work with a number of organizations where simply by automating some reporting processes – which are very easy in the scheme of things – they can save hundreds or even thousands of hours per year. That’s a huge efficiency gain! Data-driven organizations tend to automate as many of those processes as they can.

Last but certainly not least, data-driven organizations are able to go to different departments and ask the same critical business question and get the SAME answer back. In the credit union space, this could be, “How many members do you have?” In a retail environment, “How many customers purchased a product last month?”

The key point of emphasis here is that there is a clearly-defined definition for that key business term, and every member of the organization understands that definition and applies it. That’s a critical component of a data-driven organization, and we will talk about that more in later podcasts when we discuss the concept of data governance.

In summary, data-driven organizations do the following:

  • They use data instead of gut instinct to make decisions
  • They ask “What will happen next month?” instead of just asking “What happened last month?”
  • They use dashboards and visualizations to quickly digest information
  • They have all of their data in one centralized location (probably a data warehouse)
  • They use data to automate reports and processes so they are efficient as possible
  • They have good data governance where they can ask six different departments a key business question and they get the same answer back

Those are just a few of the common traits, but probably the most important traits of a data-driven organization. And that explains what does it mean to be data-driven.

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

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Subscribe and listen to the A:360 podcast on iTunes, play the YouTube video, listen to audio, or read the transcript! Next Tuesday (10/11/16) we will be releasing Podcast 001: What It Means to Be Data-Driven!

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Hey everyone!

Welcome to the first of what should be MANY podcasts in our brand new Analytics 360 or, “A:360” series.

In this A:360 series, we are going to talk about everything and anything that has to do with data and analytics. From data warehousing and some of the technical pieces that are involved with analytics to some of the more cultural and strategic aspects, like “how do you prepare your employees to be data driven?” or” what skills can you help train them on to be more analytically minded?”

Our goal for A:360 is to educate you on a number of topics in the analytics space WITHOUT boring you, and without sucking up 20 minutes of your time!

Some of our podcasts will be a short as 90 seconds. Others might go for 3 or 4 minutes. Our goal is to keep each podcast less than the length of a song. We understand that, as executives, you are busy and it is difficult to take 20 or 30 minutes out of your day to listen to a podcast. So, our goal here is to provide you with some short, easy to understand podcasts that can help you translate some of this knowledge into immediately actionable value for all of your data and analytics initiatives.

For each podcast, we’re going to obviously have the audio available in the form of a podcast, or you can get it right off of our website. We are also going to be providing two things that will be great supplements to the audio.

The first is going to be a video that will pop up some slides or some infographics that are relevant to the content we’re discussing.

We’re also going to put a transcription of this audio out there, so, if you wanted to go back and read it through and you didn’t want to have to listen to the podcast again, you can do that.

Those are two supplements we are going to provide for each podcast that I believe will bring a lot of value and allow you to consume the information in a variety of different formats.

And that wraps up our introductory podcast! Hopefully, you have a better understanding of the type of material we are going to discuss for each podcast and have a better understanding of the format that these podcasts are going to take on.

Next time, we’re going to be talking about what it means for an organization to be data-driven.

Thanks for listening to today’s A:360!

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We are incredibly excited to (finally) reveal our soon-to-be-released A:360 analytics podcast series!

What is A:360?

A:360 stands for “Analytics:360”. The podcast will discuss a variety of topics in the field of data and analytics; a 360-degree view of analytics if you will!

Our goal is to release two A:360 podcasts per week to keep content fresh, current, and consistent. Each podcast will be no less than 90 seconds and no longer than the length of a song. Designed for the busy executive, we want to provide immediately actionable information without taking up more than a few minutes of your workday or commute. A few minutes each day with us will provide you with some great insights to help your organization work towards becoming data-driven!

Topics We Will Discuss

Here are some of the few topics that we will be discussing:

  • What does it mean to become a data-driven organization?
  • What is a KPI?
  • What is a data warehouse?
  • What is an enterprise data strategy?
  • Do you have the right KPIs?
  • What does data integration matter?
  • What skills should your team learn?
  • and MANY, MANY more topics!

How Can You Stay Updated with A:360?

There are a few ways to stay connected and updated with the A:360:

1. Subscribe to our blog

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2. Subscribe to the Podcast on iTunes

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3. Head to SoundCloud

Our podcasts will be uploaded and hosted through SoundCloud. Head to this link to check out all uploads and follow us!

4. Follow Us on YouTube

Each podcast will be supplemented with a video presentation. Check out The Knowlton Group YouTube page to see our videos and subscribe!

Comments? Questions? Ideas for the podcast? Let us know by commenting below!

Let’s face it: we live in a world where a strong data and analytics competency is becoming a “must have” for successful companies. Despite the growing significance of analytics, the majority of banks and credit unions are not “data-driven” organizations.

We’ve uncovered a number of common reasons why investment in data and analytics has been pushed off or outright rejected. Despite these challenges, most of the common reasons against data and analytics are driven by inaccuracies or misinformation.

In this post, we will address the common pushbacks against data and analytics projects and how to overcome those challenges.

Becoming Data-Driven Costs Too Much Money

I’ve heard too many times that it costs “millions and millions of dollars to build a data warehouse”. This is true…if you are a larger company building a data warehouse from scratch. For most banks and credit unions, this statement is a gross exaggeration.

There are several organizations out there, like OnApproach, who have pre-built data warehouse platforms. These “pre-built data warehouses” save you a large amount of time, effort, and money. While some customization might be required to meet the specific needs of your organization, most banks and credit unions should opt for a “pre-built” solution. These “pre-built data warehouses” typically cost much less money than building a warehouse yourself.

Data warehouses costing “millions and millions of dollars” simply isn’t the case for most banks and credit unions.

Data Warehouses Are WAY Too Big of A Project…

This is also true…if you try to do everything at once. Data and analytics development should be an iterative process. If you tried to integrate all of your data sources into a data warehouse at once, it would undoubtedly be an overwhelming project. In all likelihood, building a data warehouse this way would fail. If, however, you take an iterative approach to developing your data and analytics platform, these projects are much more manageable.

Would you acquire four organizations at the same time? Of course not. Don’t bite off more than you can chew, build out your data warehouses iteratively, and these projects won’t be too overwhelming to handle.

There’s Not Enough Time to Focus on Analytics

The average $1 billion credit union we work with has somewhere between 45 and 65 third-party applications or data sources. Because of how disparate the data has become, we tend to uncover thousands and thousands of man-hours that could be automated with a better data and analytics platform. What could you do if we gave some of your staff half of their time back?

The logic that “we don’t have enough time to focus on analytics” fails when you consider how much time you could get back by investing in analytics!

The way we’ve always done it works…”

Continuing to “do things the way they’ve always been done” is a recipe for failure. With how much has changed from even ten years ago, failing to change nearly ensures your organization’s demise. Using gut instinct to make business decisions might have worked twenty years ago but not today. The rise of non-traditional competitors – like peer-to-peer lenders and 100% digital banks – was made possible by analytics. To continue to compete against these new challengers use their playbook against them and leverage the power of analytics.

Failing to incorporate analytics into your organizations’ decision making (i.e. “doing things the way you’ve always done them”) will prove to be a poor decision.

We don’t have the right culture

Most organizations that are not data-driven do not have data-driven culture. This is expected, right? Yet, how could your staff develop data-driven mentalities without having the necessary support for data and analytics to drive that cultural shift?

As part of any data and analytics initiative, you should consider how you are going to develop and foster a data-driven culture. In previous posts, we’ve provided some helpful tips on preparing your employees to become data-driven” that you should consider.

Once you begin developing your data and analytics program, you will be able to – with the right action – create and foster a data-driven culture.

Comment and let us know some of the pushback you’ve experienced when trying to leverage data and analytics!

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