Marketers – You have too many choices

I have a little secret for everyone in the world of marketing: You have too many choices.

There are way too many technology platforms in existence today. Too many ‘tools’ and too many products.  You have too many choices when it comes to getting your work done. Let’s take a quick second to glance at Scott Brinker’s MarTech 5000 landscape:

Marketing Technology Landscape Supergraphic (2018)

I’m sorry, but that’s just too many choices; especially when put in the hands of people that don’t really understand the long-term implications of multiple technology platforms.

Sure, there may be a formal selection process (in my experience, there’s not…or at least it isn’t followed) and  rarely is there a strategic vision when it comes to MarTech. There’s a bunch of tactical ‘needs’ for why a particular type of platform is needed/wanted and even a hand-wave toward ‘strategy’ but rarely is there an in-depth review of how a new platform will make things better for the marketing team and the organization as a whole and (ahem…most importantly) help reach the strategic objective of the organization.

Too many choices can be a real problem.  Need an ‘optimization’ platform for A/B testing (or other optimization issues)?  I’m sure you can find 30 or 40 vendors out there selling some version of a platform that will do what you need it to do.  Do you take the time to run a thorough selection process or do you find the first one that fits your ‘right now’ need and your budget and push ‘buy’?  Based on my experience, people do the latter and pick the first one they find that does what they need to do.  They find a solution to the problem they have today with very little to no thought put into how that platform will integrate into their broader organization’s ecosystem and/or whether the solution will solve their problem tomorrow.

Don’t get me wrong. Personally, I love the possibilities that these choices offer an organization, but only if proper governance is used when selecting and implementing these choices.  Based on my conversations with clients and marketing /  IT professionals over the last few years, there’s very little of this happening.

Over the last 3 years about half the projects I been asked to be a part of are projects to help simplify the  ecosystem within an organization.  I’ve seen companies with over 100 platforms being used within the marketing team with very few of those systems able to talk to each other — and the lives of the marketing team had become a living hell because they had too many systems, too little control of their data and too little insight into what they are able to do, how to do things and who to go to for help.

What’s the solution?

There’s not an ‘easy’ answer.

It will take hard work, focus and a real drive toward reducing the complexity within your marketing organization.  Think of it as putting your team on a diet – a MarTech diet.  When you ‘need’ (by the way – its rarely a ‘need’ and usually a ‘want’ in these cases) some new function that you just can’t live without – check your existing platforms before going out to buy some new tool. If you are absolutely sure you don’t have the functionality in your existing platforms, take a look at what you’re trying to do and think about if its an absolute need and not just a ‘want’.  More importantly, think about the long term vision / strategy of the organization – how does ‘MarTech Platform X’ get you there?  If you can’t easily answer the question, it might be best to try to find a way to do what you need to do with your existing ecosystem.

 

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?

Big Data Roadmap – A roadmap for success with big data

Big Data RoadmapI’m regularly asked about how to get started with big data. My response is always the same: I give them my big data roadmap for success.  Most organizations want to jump in a do something ‘cool’ with big data. They want to do a project that brings in new revenue or adds some new / cool service or product, but I always point them to this roadmap and say ‘start here’.

The big data roadmap for success looks starts with the following initiatives:

  • Data Quality / Data Management systems (if you don’t have these in place, that should be the absolute first thing you do)
  • Build a data lake (and utilize it)
  • Create self-service reporting and analytical systems / processes.
  • Bring your data into the line-of-business.

These are fairly broad types of initiatives, but they are general enough for any organization to be able to find some value.

Data Management / Data Quality / Data Governance

First of all, if you don’t have proper data management / data quality / data governance, fix that. Don’t do anything else until you can say with absolute certainty that you know where your data has been, who has touched your data and where that data is today. Without this first step, you are playing with fire when it comes to your data. If you aren’t sure how good your data is, there’s no way to really understand how good the output is of whatever data initiative(s) you undertake.

Build a data lake (and utilize it)

I cringe anytime I (or anyone else) says/writes data lake because it reminds me too much of the data warehouse craze that took CIO’s and IT departments by storm a number of years ago. That said, data lakes are valuable (just like data warehouses where/are valuable) but it isn’t enough to just build a data lake…you need to utilize it. Rather than just being a large data store, a data lake should store data and give your team(s) the ability to find and use the data in the lake.

Create self-service reporting and analytical systems / processes.

Combined with the below initiative or implemented separately, developing self-service access and reporting to your data is something that can free up your IT and analytics staff. Your organization will be much more efficient if any member of the team can build and run a report rather than waiting for a custom report to be created and executed for them. This type of project might feel a bit like ‘dashboards’ but it should be much more than that – your people should be able to get into the data, see the data and manipulate the data and then build a report or visualization based on those manipulations. Of course, you need a good data governance process in place to ensure that the right people can see the right data.

Bring your data into the Line of Business

This particular initiative can be (and probably should be) combined with the previous one (self-service), but by itself it still makes sense to focus on by itself. By bringing your data into the line of business, you are getting it closer to the people that best understand the data and the context of the data. By bringing data into the line of business (and providing the ability to easily access and utilize said data), you are exponentially growing the data analytical capabilities of your organization.

Big Data Roadmap – a guarantee?

There’s no guarantee’s in life, but I can tell you that if you follow this roadmap you will have a much better chance at success than if you don’t.  The key here is to ensure that your ‘data in’ isn’t garbage (hence the data governance and data lake aspects) and that you get as much data as you can in the hands of the people that understand the context of that data.

This big data roadmap won’t guarantee success, but it will get you further up the road toward success then you would have been without it.

 

Will marketing continue to grow their share of the technology budget?

marketing technology budgetIf you are involved with marketing and/or technology in any large organization, you most likely are hearing a lot about marketing technology (MarTech) and the ‘explosion’ of MarTech people, technology, projects….and budgets. There are some folks out there who claim that within a few years, marketing will be spending more on IT than the CIO but many IT professionals I speak with just have a hard time accepting that marketing will ever drive more spend on tech than IT does/will.

My response to these folks is simple: It is already here.

Let’s take a second to think about some of the higher priority items for the IT group. According to the 2016 State of the CIO report from IDG, the top 3 priority items for CIO’s in the coming year are:

  • Complete a major enterprise project
  • Help reach a specific goal for corporate growth
  • Upgrade IT security

Those first two items are pretty broad and specific to each organization, but they could easily relate to MarTech projects. For the last item on IT security upgrades, the report says that about 12% of the IT budget will be spent on upgrading IT security (which seems low to me, but I’m not as close to security as I am to other parts of IT).

Another nice little nugget of knowledge from the 2016 State of the CIO report is the following:

57% of the total dollars invested in tech is now directly controlled by IT (and is expected to grow to 59% in the next 3 years).

One thing to notice with this particular stat – while the majority of budget is controlled by IT, there’s no real detail in the report on where or how that money is spent other than to say that 33% of marketing’s budget is currently set aside for technology.

All the numbers and data points mean very little without some context, which is what many IT professionals (and non-marketing people) lack.  The spend on MarTech five years ago was generally small. Marketing groups would spend money on content management systems, web analytics platforms and e-commerce systems but very few were investing money in large, enterprise-level systems. That has changed in most medium to large organizations. Today, marketing organizations are spending money are those enterprise systems because they now understand how important an integrated, enterprise platform can be for driving engagement and revenue.

Will marketing continue to grow their technology budget? I think so.  Will IT professionals continue to complain about that? Probably….but the majority will realize how important MarTech spend is to the future of the organization.

Complexity – The Killer of Agility?

complexityI’ve said a few times that the data center of today isn’t the data center of yesterday nor is it the data center of tomorrow. In fact, in “The Data Center of Tomorrow” I wrote that the: “data center will be a combination of internal and external systems that combine to create an agile, efficient and effective technology delivery platform.”

While I believe that will be the case in the near future (if it isn’t already the case today), the data center of tomorrow has the potential to add complexity to the organization’s IT systems and platforms. This complexity may just be a simple replacement of other types of complexity or it may be adding complexity to the data center. Either way, complexity has the potential to be an agility killer if it isn’t managed or planned for correctly.

Complexity has always existed within the data center. From the first day of data center existence, IT professionals have had to manage complexity but in recent years there’s been quite a bit of growth in complex systems within the data center. With companies increasing their use of virtualization within their data centers, connecting data centers with the cloud and implementing new platforms and systems every year, the level of complexity continues to increase.

Without proper thinking and planning, this complexity can have a negative effect on agility within the data center. There are a few things that organizations can do to attempt to manage complexity within the data center while keeping agility at the forefront of the IT group and the data center. A few ideas for managing complexity are:

  • Get visibility into the platforms throughout the organization to ensure that the IT group understands what platforms the business has
  • Get visibility beyond the platforms to allow IT to understand the business processes that are driving platform changes
  • Ensure open communication channels between all groups within the business to ensure when a new platform is needed or wanted, IT is informed and involved in the decision making process
  • Have a proper business technology strategy that drives all technology projects.
  • Build a technology council and invite members from all areas of the business to allow different opinions and insights into the technology strategy of the organization

As you can see from these ideas may not seem that great at first, they are a starting point for understanding the technological systems and platforms within the company. By understanding your platforms, you can understand the complexity that exists (or might exist in the future) and help keep agility alive.

This post is brought to you by Symantec and The Transition To The Agile Data Center.

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