Eric D. Brown, D.Sc.

Data Science | Entrepreneurship | ..and sometimes Photography

Tag: data (page 1 of 10)

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

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

Want to speed up your digital transformation initiatives? Take a look at your data

Digital Transformation imageDigital transformation has taken center stage in many organizations. Need convincing?

  • IDC predicts that two-thirds of the CEOs of Global 2000 companies will have digital transformation at the center of their corporate strategies by the end of 2017.
  • Four in 10 IT leaders in the Computerworld 2017 Tech Forecast study say more than 50% of their organization has undergone digital transformation.
  • According to Gartner, CIOs are spending 18% of their budget on digitization efforts and expect to see that number grow to 28% by 2018.

Based on this data (and in my regular talks with CIOs), there’s a high probability that you have an initiative underway to digitize one or more aspects of your organization. You may even be well along the digital transformation path and feeling pretty good about your progress.  I don’t want to rain on your digital transformation parade, but before you go any further on your journey, you should take a long, hard look at your data.

Data is the driving force behind every organization today, and thus the driving force behind any digital behind any digital transformation initiative. Without good, clean, accessible, and trustworthy data, your digital transformation journey may be a slow (and possibly difficult) one.  Leveraging data to help speed up your digital transformation initiatives first requires proper data management and governance. Once that’s in place, you can begin to explore ways to open up the data throughout the organization.

Digital transformation is doomed to fail if some (or all) of your data is stored in silos.  Those data silos may have worked great for your business in the past by segmenting data for ease of management and accessibility, but they have to be demolished in order to compete and thrive in the digital world.  To transform into a truly digital organization, you can no longer allow marketing’s data to remain with marketing and finance data to remain within finance. Not only do these data silos make data management and governance more complex, they are challenges to the types of analysis that deliver new insights into the business (e.g., analyzing revenue streams by looking at new ways of combining marketing and financial data).  Data needs to be accessible using modern data management, data governance and data integration systems (with the proper security protocols in place) in order to make data accurate and usable to be a used as a driving force for digital transformation.

Removing data silos is just one aspect of the required data management and governance needed for driving digital transformation.  Implementing data management and governance systems and processes that allow your data to remain secure while remaining available for analysis is a building stone for digital transformation.

In order to speed up your transformation projects and initiatives, you really need to take a long, hard look at your data. If you have good data management and governance throughout your organization, you are one step ahead of those companies that haven’t focused on managing their data as a strategic asset rather than allowing data to be hoarded and live in silos around the organization.

Digital transformation will be one of the key areas of focus for CIOs for some time to come and it just might just be the key to remaining competitive in your market, so anything you can do today to help your transformation projects succeed should be immediately considered.  Having a good data management and governance plan and system in place should help drastically speed up your digitization initiatives.

Originally published on CIO.com

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

You Need a Chief Data Officer. Here’s 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 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

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?

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