Turn your data geeks into customer geeks

an image that says 'I love data"What would you do if you had so much data about your customers that you know could know (almost) everything about your customer when they contacted you? Better yet, what if you had the ability to instantly know the exact offer for service or product that would pitch the right ‘sales’ approach that your customer would immediately sit up, take notice and spend money?

Most of you would jump at the chance to have this information about your clients.  You may be willing to open up the checkbook for a huge amount of money to make this happen.  What if I told you that you don’t need to do much more than get a better grasp on your data and understand how to use that data to build a 360 degree view of your customer?

Granted, you may need to collect a bit more data (and perhaps find new types of data) and you may need to implement some new data management processes and/or systems, but you shouldn’t have to start from scratch  – unless you have no data skills, people or processes. For those companies that already have a data strategy and a team of data geeks, building a customer-centric view with data can be extremely rewarding.

Many companies consider themselves ‘customer-centric’ and have built programs and processes in order to ‘focus on the customer.  They may have done a very good job in this regard but there’s more than can be done. Most organizations have focused on Customer Relationship Management (CRM) as a way to help drive interactions with clients.  While a CRM platform is important and necessary, most of these platforms are nothing more than data repositories that provide very little value to an organization beyond the basics of ‘we talked to this person’ or ‘we sold widget X to that customer.’

Utilizing proper data management and the data lake concept, companies can begin to build much broader viewpoints into their customer base. Using data lakes filled with CRM data along with customer information, social media data, demographics, web activity, wearable data and any other data you can gather about your customers you (with the help of your data science team) can begin to build long-term relationships built on more than just some basic data.

In addition to better relationships with your customers, a data-centric approach can help you better predict the activities of your customers, thereby helping you better position your marketing and messaging. Rather than hope your messaging is good enough to reach a small percentage of your customer base, the data-centric approach can allow you to take advantage of the knowledge, skills and systems available to you. Additionally, this approach will allow your data team to create personal and individual programs and messaging to help drive marketing and customer service.

Originally published on CIO.com

Opportunity Lost: Data Silos Continue to inhibit your Business

An image of data silosAccording to some estimates, data scientists spend as much as 80% of their time getting data in a format that can be used. As a practicing data scientist, I’d say that is a fairly accurate estimate in many organizations.

In the more sophisticated organizations that have implemented proper data integration and management systems, the amount of time spent sifting through and cleaning data is much lower and, in my experience, more in line with the numbers reported in the 2017 Data Scientist Report by Crowdflower.

That report indicates a better balance between basic data-wrangling activities and more advanced analysis:

  • 51% of time spent on collecting, labeling, cleaning and organizing data
  • 19% of time spent building and modeling data
  • 10% of time spent mining data for patterns
  • 9% of time spent refining algorithms

Closing the Gaps

If we think about this data transformation in terms of person-hours, there’s a big difference between a data scientist spending 80% of their time finding and cleaning their data and a data scientist spending 51% of their time on that same tasks. Closing the gap begins with demolishing the data silos that impede organization’s’ ability to extract actionable insights from the data they’re collecting.

Digital transformation projects have become a focus of many CIOs, with the share of IT budgets devoted to these projects expected to grow from 18% to 28% in 2018. Top-performing businesses are allocating nearly twice as much budget to digital transformation projects – 34% currently, with plans to increase the share even further to 44% by 2018.

CIOs in these more sophisticated organizations – let’s call them data-driven disruptors – have likely had far more success finding ways to manage the exponential growth and pace of data. These CIOs realize the importance of combating SaaS sprawl, among other data management challenges, and have found better ways to connect the many different systems and data stores throughout their organization.

As a CIO, if you can free up your data team(s) from dealing with the basics of data management and let them focus their efforts on the “good stuff” of data analytics (e.g., data modeling, mining, etc.), you’ll begin to see your investments in big data initiatives deliver real, meaningful results.

Originally published on CIO.com

Customer Engagement: A Data-Driven Team Sport

Customer Engagement: A Data-Driven Team SportWhat would you do if you had so much data about your customers that you know could know (almost) everything about your customer when they contacted you? Better yet, what if you had the ability to instantly know the exact offer for service or product that would pitch the right ‘sales’ approach that your customer would immediately sit up, take notice and spend money?

Most of you would jump at the chance to have this information about your clients.  You may be willing to open up the checkbook for a huge amount of money to make this happen.  What if I told you that you don’t need to do much more than get a better grasp on your data and understand how to use that data to build a better overall view of your customer?

Granted, you may need to collect a bit more data (and perhaps find new types of data) and you may need to implement some new data management processes and/or systems, but you shouldn’t have to start from scratch unless you have no data skills, people or processes. For those companies that already have a data strategy and a team of data geeks, building a customer-centric view with data can be extremely rewarding.

This customer-centric, data-driven approach is what most organizations are driving toward with their digital transformation initiatives.  Graeme Thompson, Informatica CIO, has argued for the importance of a customer-centric approach for some time. According to Graeme:

“You have to think about [digital transformation] in a connected way across the entire company.  It’s no longer about executing brilliantly within one functional silo. CIOs see the end-to-end connection [of different functions] across the entire company – how all these different processes need to work together to optimize the outcome for the enterprise, and, most importantly, for customers.”

Many companies consider themselves ‘customer-centric’ and have built programs and processes in order to ‘focus on the customer.  They may have done a very good job in this regard but there’s more than can be done. Most organizations have focused on Customer Relationship Management (CRM) as a way to help drive interactions with clients.  While a CRM platform is important and necessary, most of these platforms are nothing more than data repositories that provide very little value to an organization beyond the basics of ‘we talked to this person’ or ‘we sold widget X to that customer.’

These ‘customer-centric’ companies can be even more custome​_r-centric by becoming a data-driven organization. They have taken a small subset of customer data and built their entire customer engagement process around that data set.  That approach has worked OK for years, but with the data available to companies today, there’s no need to rely solely on that small data set.

Utilizing proper data management and the data lake concept, companies can begin to build much broader viewpoints into their customer base. Using data lakes filled with CRM data along with customer information, social media data, demographics, web activity, wearable data and any other data you can gather about your customers you (with the help of your data science team) can begin to build long-term relationships built on more than just some basic data.

In the white paper titled ‘Game Changers: Meet the Experts Behind Customer 360 Initiatives,’ there are some very good examples of how companies have become much more customer-centric and data-driven.  A few examples from the paper are:

  • FASTWEB uses Salesforce as much more than just a CRM. Their Salesforce instance includes a view into the customer by providing lists of latest invoices, the status of those invoices, payments and other key customer relationship data.
  • PostNL, a mail, parcel and e-commerce company, has changed their focus from simple ‘addresses’ to one that is focused on the customer by focusing first on data, then on the customer. No longer is their focus on getting a package from point-A to point-B, it is on using data to ensure the customer’s needs are met.
  • Bradley Corporation, a 95-year old manufacturer of plumbing fixtures implemented a Product Information Management system to ensure that data is up-to-date and accessible for their more than 200,000 products. This system simplifies the ability for their customers to find the right parts quickly and easily.

In addition to better relationships with your customers, a data-centric approach can help you better predict the activities of your customers, thereby helping you better position your marketing and messaging. Rather than hope your messaging is good enough to reach a small percentage of your customer base, the data-centric approach can allow you to take advantage of the knowledge, skills and systems available to you and your data team to create personal and individual programs and messaging to help drive marketing and customer service.

Originally published on CIO.com

Machine Learning Is Transforming Data Security

Machine Learning Is Transforming Data SecurityData is the lifeblood of any organization today so it should be easy to understand that security of that data is just as important (if not more important) that the data itself. It seems that data security (or rather the lack thereof) has been in the news regularly over the last few years. The inability for organizations to secure their has caused millions (if not billions) of dollars in damages from lost revenue in addition to the loss of trust.   A machine learning approach will never fully replace a human in the security chain, but it can help IT professionals monitor IT system and data security as well as monitor who (and how) data is accessed and used throughout the organization.  

Throughout the many different IT departments I’ve talked with over the years, I haven’t met any IT professional in an enterprise organization who wasn’t interested in ensuring enterprise security is intact. Organizations have spent considerable amount of time, effort and money to implement the proper security systems and protocols but most IT professionals are still worried about data security.   

That said, only a small percentage of these same security conscious people have systems or processes in place that accurately and quickly monitor how secure their data is.  In my experiences, sensitive data in most organizations is generally secure but isn’t regularly monitored or audited due to the costs and time commitment needed for analyzing access patterns and ensuring there’s been no intrusions.  In fact, in many organizations, IT professionals would be unable to provide a clear location of sensitive data throughout their organization.

In a Ponemon report titled ‘The State of Data Centric Security’, 57% of survey respondents report see their biggest security risk being that they don’t understand where their sensitive data lives. According to that same report, most IT professionals (79% of respondents) believe that not knowing where their sensitive data lives is a big security concern but only a small majority (51% of respondents) believe that it should be a priority to protect and secure their sensitive data. This gap is problematic and will cause significant issues for organizations.

Data has been – and will continue to be – a large part of most organizations’ digital transformation strategy. That said, this data is also creating new vulnerabilities without the property security systems and process in place. Graeme Thompson, CIO of Informatica, argues this point very well in Data Security: Don’t Call an Ambulance for a Sore Throat when he writes:

Just as businesses have evolved toward the cloud, they’re also evolving toward enterprise-wide data access. We recognize the valuable insights and innovations to be gleaned from trading siloed departmental data warehouses for the comprehensive enterprise data lake. Tearing down those silos can cost us a layer of security around specific data sets, but curling up in an information panic room is not the way forward.

Last year, I was speaking with the CISO for a large enterprise organization. The conversation was around how much time they’ve been spending on thinking about and securing their IT systems and their data. This particular CISO has done a very good job of implementing master data management systems and processes to ensure their data is safe, accurate and available. Though he has done an admirable job, he worries that he doesn’t have the manpower or budget to feel comfortable that the organization’s data is as secure as it can be.

With the large amounts of both structured and unstructured data in most organizations, some of the older IT security approaches may not work as well as they might have in the past.  My suggestion to this CISO was to spend some time investigating the use of machine learning approaches to data security. Machine learning can provide an organization with a ‘second set’ of eyes and ears that can be focused on data security. Implementing machine learning systems can not only free  up team members to focus on other things but – more importantly – these systems can monitor threats and issues at a scale that humans just can’t replicate.

The CISO I mentioned earlier is currently trialing an approach that uses machine learning security monitoring system for both his IT systems and his various data stores and, even though this system has only been in place for less a few months, he’s already begun to see efficiency improvements for security monitoring across the enterprise.  As an example, after only a few days of their new machine learning enabled security platform being in place, they were seeing hundreds of issues through their monitoring systems that they hadn’t been able to capture before. From these efficiencies, he’s been able to re-assign one of his IT personnel from full-time security monitoring to a less than full-time role because the monitoring has been capable of raising alerts in real-time without any manual intervention.

In addition to the act of monitoring for intrusions and security issues, these machine learning systems can help IT professionals locate and manage their sensitive data, recommend remediation efforts and actions when issues are found and gain a better understanding of who is accessing and using data across the organization.

Like many other areas within the modern organization, machine learning is changing how companies approach data security and changing data security itself. Machine learning isn’t a panacea for security, but it is is a very good tool to have in your security tool box.

Originally published on CIO.com

Data Maturity before Digital Maturity

Data MaturityI recently wrote about Digital Maturity vs Digital Transformation where I proclaimed that its more important to set your goal for digital maturity rather than just push your organization toward digital transformation initiatives.  In this post, I want to talk about one of the most important aspects of digital maturity: Data Maturity. Before you can even hope to be digitally mature, you must reach data maturity.

What is Data Maturity?

Data maturity is the point at which you’ve been able to thoroughly and explicitly answer the ‘who, what, where, when and how’ of your data.  You’ve got to understand the following:

  • where the data came from?
  • where is it stored (and where has it been stored)?
  • how it was collected?
  • how it will be accessed?
  • who will access it?
  • who has had access to it over its lifetime?
  • what type of data is it?
  • if personal data, what types of permissions do you have to use it?
  • when was the data collected?
  • when was the data last reviewed?
  • when was the data last accessed?
  • how do you know the data is accurate?

There are many more questions to ask / answer in the ‘who, what, where, when and how’ universe, but hopefully you get the point. If you can’t answer these questions to build up your data’s “metadata”, then you haven’t reached maturity.

Data maturity requires proper data governance, data management and proper data processes (see previous writings here on those topics).   Like I’ve said before, i’m not an expert in these areas but I do know good data management when I see it – and most organizations don’t have good data management practices/processes.

Data Maturity is more than just technology initiatives though. Its more than having the right systems in place. Data Maturity requires organizational readiness as well as technology readiness; and the organizational readiness is generally the harder of the two data maturity paths to complete.

I’m not going to get into organizational readiness vs technology readiness in this post (I’ll save it for a later post) but just know that there are a lot of parallel paths (and sometimes perpendicular paths) that you need to take to get to digital maturity – and data maturity is one of the important aspects to focus on while working toward that digital maturity goal.

Are you working towards data maturity along the path to digital maturity?

More isn’t always better

More is always better?More is always better right?

More feedback from clients can help you improve your service.  More money can help you build better products and teams. More data can help you make better decisions.  More resolution can make your photos better.

More is always better isn’t it?

Well. No. More isn’t always better.

Seth Godin recently said that “Too much resolution stops giving you information and becomes merely noise, which actually gets in the way of the accuracy you seek.”

This is very true. Anyone that’s ever worked with data will tell you that more data just means more work. Sure, you may find a great nugget in that additional data, but that extra data doesn’t always equate to more knowledge but it always equates to more work.

To Seth Godin’s point, more ‘resolution’ isn’t always the answer either. I can go buy a $50,000 camera with the highest resolution possible and still make terrible photos. Just because I have the resolution available to me doesn’t mean I have the lenses available to take advantage of that resolution nor does it mean I have the talent to utilize the high resolution.

More isn’t always better.  Adding more data to your already large data set isn’t going to find the answer for you. It might help you find more questions to ask, but it doesn’t guarantee that an answer will be found.

Rather than go spend $50K on the ‘best’ camera, spend $500 on an OK camera and learn the skills and methods  needed to make the most of what you have. When you’ve mastered your ‘art’, then move up to something more expensive with more functions.

Rather than focus on gathering more data, you need to be focused on using the data you have in the most optimal way possible. Make sure you have the tools and skills in place to analyze / use what you have before you go and add ‘more’ to the mix.

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