Is your data ready to help you make game-changing decisions?

Decisions sign in the sky

Organizations today are facing disruption on all fronts, which should viewed as a good thing as it allows organizations to redefine their strategies, their markets and re-create their organization to be better prepared for the future.

This disruption is one of the driving factors behind digital transformation initiatives. In order to successfully complete these transformation projects, companies must build a foundation of properly managed data.  With the right data management and governance systems and processes in place, CIO’s can begin to build an intelligent organization that has the capability to make intelligent decisions based on data that is reliable, up-to-date and trustworthy.

To build the right foundation for an effective data-driven digital transformation, CIOs must first ensure their organization can effectively understand and manage their data. With the proper data management platform in place to support the discovery, connectivity, quality, security, and governance across all systems and process, organizations can fully trust their data, which means they can trust the outcome of any decisions, processes, and outcomes driven through that data.

Reliable data has always been important, but it’s vitally important for organizations looking to unlock its potential as a driver of digital transformation. With high-quality, “clean” data, CIOs can begin to build an intelligent organization from top to bottom by providing trustworthy data, information, and knowledge for all aspects of the business.

An evolved approach to data management sets the stage for improvements across all areas of the business including finance, marketing and operations. In describing how proper data management has helped her company, Cynthia Nustad, CIO for HMS, states a few clear business benefits. “We’ve accelerated new product introduction, aligned data easier, and reduced the time to onboard customer data by more than 40%,” she says.

In addition to the improvements that data quality can bring to your existing operations, good data provides a strong base for entering the intelligence age. With good data, you can begin to build new data analytics projects and platforms, and incorporate machine learning and other forms of artificial intelligence (AI) into your analytics toolkit. If you try to implement these types of projects without proper data quality and governance systems and processes, you’ll most likely be wasting time and money.

While it’s tempting for CIOs to jump headfirst into AI and other advanced big data initiatives, successful deployments first require a focus on data management. It isn’t the most exciting area, but having good data is an absolute requirement to building an intelligent organization.

Originally published on CIO.com

Opportunity Lost: Data Silos Continue to inhibit your Business

An image of data silos

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

You Need a Chief Data Officer. Here’s Why.

Image of the word "why"

Image of the word "why"Big data has moved from buzzword to being a part of everyday life within enterprise organizations. An IDG survey reports that 75% of enterprise organizations have deployed or plan to deploy big data projects. The challenge now is capturing strategic value from that data and delivering high-impact business outcomes. That’s where a Chief Data Officer (CDO) enters the picture. While CDO’s have been hired in the past to manage data governance and data management, their role is transitioning into one focused on how to best organize and use data as a strategic asset within organizations.

Gartner estimates that 90% of large global organizations will have a CDO by 2019. Given that estimate, it’s important for CIOs and the rest of the C-suite to understand how a CDO can deliver maximum impact for data-driven transformation. CDOs often don’t have the resources, budget, or authority to drive digital transformation on their own, so the CDO needs to help the CIO drive transformation via collaboration and evangelism.

“The CDO should not just be part of the org chart, but also have an active hand in launching new data initiatives,” Patricia Skarulis, SVP & CIO of Memorial Sloan Kettering Cancer Center, said at the recent CIO Perspectives conference in New York.

Chief Data Officer – What, when, how

A few months ago, I was involved in a conversation with the leadership team of a large organization. This conversation revolved around whether they needed to hire a Chief Data Officer and, if they did, what that individual’s role should be. It’s always difficult creating a new role, especially one like the CDO whose oversight spans multiple departments. In order to create this role (and have the person succeed), the leadership team felt they needed to clearly articulate the specific responsibilities and understand the “what, when, and how” aspects of the position.

The “when” was an easy answer: Now.

The “what” and the “how” are a bit more complex, but we can provide some generalizations of what the CDO should be focused on and how they should go about their role.

First, as I’ve said, the CDO needs to be a collaborator and communicator to help align the business and technology teams in a common vision for their data strategies and platforms, to drive digital transformation and meet business objectives.

In addition to the strategic vision, the CDO needs to work closely with the CIO to create and maintain a data-driven culture throughout the organization. This data-driven culture is an absolute requirement in order to support the changes brought on by digital transformation today and into the future.

“My role as Chief Data Officer has evolved to govern data, curate data, and convince subject matter experts that the data belongs to the business and not [individual] departments,” Stu Gardos, CDO at Memorial Sloan Kettering Cancer Center, said at the CIO Perspectives conference.

Lastly, the CDO needs to work with the CIO and the IT team to implement proper data management and data governance systems and processes to ensure data is trustworthy, reliable, and available for analysis across the organization. That said, the CDO can’t get bogged down in technology and systems but should keep their focus on the people and processes as it is their role to understand and drive the business value with the use of data.

In the meeting I mentioned earlier, I was asked what a successful Chief Data Officer looks like. It’s clear that a successful CDO crosses the divide between business and technology and institutes data as trusted currency that is used to drive revenue and transform the business.

Originally published on CIO.com.

Customer Engagement: A Data-Driven Team Sport

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 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 Quality – The most important data dimension?

data quality

data qualityIn a recent article I wrote over on CIO.com titled Want to Speed Up Your Digital Transformation Initiatives? Take a Look at Your Data, I discuss the importance of data quality and data management in an organization’s digital transformation efforts.  That article can be summarized with the closing paragraph (but feel free to go read the full version):

To speed up your transformation projects and initiatives, you need to take a long, hard look at your data. Good data management and governance practices will put you a step ahead of companies that don’t yet view their data as a strategic asset.

I wanted to highlight this, because it continues to be the biggest issue I find when working with clients today. Many organizations have people that are interested in data and they are finding the budget to get their team’s up to speed on data analytics and data science…but they are still missing the boat on the basics of good data management and data quality.

What is data quality?

Informatica defines data quality in the following manner:

Data quality refers to the overall utility of a dataset(s) as a function of its ability to be easily processed and analyzed for other uses, usually by a database, data warehouse, or data analytics system. … To be of high quality, data must be consistent and unambiguous. Data quality issues are often the result of database merges or systems/cloud integration processes in which data fields that should be compatible are not due to schema or format inconsistencies

Emphasis mine.

Not a bad definition. My definition of data quality is:

Data quality is both simultaneously a measurement and a state of your data. It describes the consistency, availability, reliability, usability, relevancy, security and audibility of your data.

Now, some may argue that this definition covers data management and data governance more than data quality…and they may be correct…but I’ve found that most people that aren’t ‘data people’ get really confused (and bored) when you start throwing lots of different terms out there at them so I try to cover as much of the master data management world under data quality. I’ve found its more relatable to most folks when you talk about ‘data quality’ vs ‘data governance’, etc.

Data quality in the real world

Last month, I spoke to the CEO and CIO of a medium sized company about a new data initiative they are planning.  The project is a great idea for them and should lead to some real growth in both revenue and data sophistication. While I won’t go into the specifics, they are looking to spend a little over $5 million in the next two years to bring data to the forefront of all of their decision making process.

While listening to their pitch (yes…they were pitching me…I’m not used to that) I asked one my ‘go-to’ questions related to data quality. I asked:  “Can you tell me about your data quality processes/systems?” They asked me to explain what I meant by data quality. I provided my definition and spent a few minutes discussing the need for data quality.  We spoke for an hour about data management, data quality and data governance. We discussed how each of these would ‘fit’ into their data initiative(s) and what additional steps they need to take before they go full-speed into the data world.

Early today I had a follow up conversation with the CEO. She told me that they are moving forward with their data initiative with a fairly large change – the first step is implementing proper data management / quality processes and systems.   Thankfully for this organization both the CEO and CIO are smart enough to realize how important data quality is and how important having quality data to feed into their analysis process/systems is for trusting that analysis that comes from their data.

As I said in the CIO.com article: ‘Good data management and governance practices will put you a step ahead of companies that don’t yet view their data as a strategic asset.’ This CEO / CIO pair definitly see data as a strategic asset and are willing to do what it takes to make quality, governance and data management a part of their organization.