In my initial blog in this three-part series (“Is Digital Transformation the path for your company?”), you read about the benefits and challenges experienced in driving digital transformation in your business. In this second blog, I will dive deeper into the topic and discuss the role data analytics plays in transforming your business for building a more competitive future.
Increasingly, connected systems and devices are generating data that produce insights for improving business processes and consumer experiences. IDC predicts that by the year 2020 there will be 44 zettabytes (that’s 44 x 1021) of information, spawned partly by consumer activity but mostly from a myriad of growing devices. Introducing… the Internet of Things — an ecosystem of sensors, machines, and other everyday items that are producing data and, in many cases, interacting with each other. With more data come more opportunities. Big data powers new possibilities for organizations — to gather, analyze and act in near-real-time. Leveraging insights generated from all their data sources, organizations can monetize new opportunities, enhance customer experiences and optimize key business processes.
The benefits of data analytics are obvious. Transforming data into actionable insights to achieve business outcomes faster and more efficiently is becoming a differentiator. According to Bain & Company, organizations with advanced analytic capabilities are two times more likely to be in the top quartile of financial performers for their industry. However, creating insights from data is only half of the battle, as now those insights must be operationalized back into the business in order for the impact to be made, and digital transformation realized.
Integrating data analytics into your digital transformation will be more effective when the challenges faced by most businesses are addressed early. There are three categories of challenges you need to focus on: operational, technological and organizational.
Before you embark on your digital transformation through data analytics, like any other project, or like a family road trip, you must determine what will be your end-goal — which use case or business outcome do you want to address. This sets your target and keeps your team focused while you plan the attack using technology and internal resources. Those resources include determining which data sets are more appropriate to work with. Understanding which data sets you need will help your organization to uncover where most of the data exists — often in disparate storage locations — and determine how to address consolidating, cleansing and wrangling them when needed.
Some businesses find choosing the first use case to be challenging. Here are the top three common use cases for data analytics, by vertical industry, according to IDC:
- Healthcare: Clinical research optimization, patient segmentation, genome analysis/DNA sequencing
- Telecommunications: Revenue assurance, location-based security analysis, price optimization
- Financial Services: Location-based security analysis, algorithmic trading, influencer analysis
IDC also outlines common business outcomes across these same verticals: enable IT optimization; improve operational efficiencies, fraud, and risk management; improve business processes and operations; improve customer service; and support and implement regulatory compliance.
There are 3Vs that define properties or dimensions of big data: Volume, Variety and Velocity. Understanding the use case will answer what types and volume of data would be required for accurate analytics, and how rapidly that data will arrive to be processed. With use cases defined, businesses can use technology to determine the path to achieve the target and set up guide rails for the trip.
To support efficient data analytics it is important to address the challenge of disparate data storage locations. Data consolidation is a key requirement. Creating a data repository, or data lake, with a single view of available data, would reduce the time spent discovering and indexing data sets by data science teams. It would also support future analytics programs when new use cases are considered.
Creating an environment that allows data analytics teams to work quickly and efficiently while maintaining control has always been a challenge for the IT department. To address this challenge, businesses are beginning to move toward a self-service environment model to allow teams to access the data and infrastructure they need, while providing quotas that limit any one project from monopolizing resources. In offering this new self-service approach, IT will be seen as a champion in the organization by supporting the fast pace of business while making sure the play stays inside the sandbox, so to speak.
Using open-source tools or platforms ensures your program can scale without being locked to vendor-specific applications, yet they might not deliver the robust outcome you desire. Digital transformation is a strategic plan and a long one, which requires you to plan ahead for the future of your data analytics program, beyond experimentation. With that in mind, consider an engineered solution that features an open-platform that allows your team to use analytic tools that are most familiar to them.
Finally, data governance and security is extremely important and must be planned to cover all parts of the data analytics lifecycle, even down to the field-level. The possibility of an employee or even a hacker gaining access to confidential data makes violation of compliance policies a greater and possibly expensive risk to the company. Choose a technology that has a role-based configuration so one data analyst may see all of a social security number, for instance, while another one is limited to the last four digits.
So it would seem that with all of the business outcomes and technology issues out of the way, organizations are ready for data analytics success, correct? Not so fast. One of the key components to every successful venture is the most important resources a business has — its people.
Getting and keeping the talent necessary to make use of data analytics is quickly becoming more challenging as the demand grows. For example, one consideration is the skillset with specific analytics packages and programming languages. Choosing a certain technology may require a focus on only those analysts who have experience with SAS Analytics or Java, which could result later in barriers to program expansion. As mentioned above, an open-platform that allows flexibility to use analytic tools of choice would allow a greater breadth of skillsets to choose from when recruiting new team members.
Finally, with data analytics it is very important to create a bridge between the business side and IT in an organization, to ensure its success. Build teams that include members from across the organization and have them vested in the success of the program. When business and IT are aligned, together they will understand and address what the company needs to be more competitive, to engage its customers better, and to create new disruptive business models.
We will wrap this discussion up with a final blog — “IT as the Champion of Your Digital Transformation” — so stay tuned for more.
William Geller is Principal Product Marketing for Data Analytics at Dell EMC.
 “Creating Value Through Advanced Analytics,” Bain & Co, 2015