Understanding Data Roles

AnalyticsAnywhereWith the rise of Big Data has come the accompanying explosion in roles that in some way involve data. Most who are in any way involved with enterprise technology are at least familiar with them by name, but sometimes it’s helpful to look at them through a comprehensive lens that shows us how they all fit together. In understanding how data roles mesh, think about them in terms of two pools: one is responsible for making data ready for use, and another one that puts that data to use. The latter function includes the tightly-woven roles of Data Analysts and Data Scientist, and the former includes such roles as Database Administrator, Data Architect and Data Governance Manager.

Ensuring the data is ready for use

Making Sure the Engine Works.

A car is only as good as its engine, and according to PC Magazine the Database Administrator (DBA), is “responsible for the physical design and management of the database and for the evaluation, selection and implementation of the DBMS.” Techopedia defines the position as one that “directs or performs all activities related to maintaining a successful database environment.” A DBA’s responsibilities include security, optimization, monitoring and troubleshooting, and ensuring the needed capacity to support activities. This of course requires a high level of technical expertise–particularly in SQL, and increasingly in NoSQL. But while the role may be technical, TechTarget maintains that it may require managerial functions, including “establishing policies and procedures pertaining to the management, security, maintenance, and use of the database management system.”

Directing the Vision. With the database engines in place, the task becomes one of creating an infrastructure for taking in, moving and accessing the data. If the DBA builds the car, then the Enterprise Data Architect (EDA) builds the freeway system, laying the framework for how data will be stored, shared and accessed by different departments, systems and applications, and aligning it to business strategy. Bob Lambert describes the skills as including an understanding of the system development life cycle; software project management approaches; data modeling, database design, and SQL development. The role is strategic, requiring an understanding of both existing and emerging technologies (NoSQL databases, analytics tools and visualization tools), and how those may support the organization’s objectives. The EDA’s role requires knowledge sufficient to direct the components of enterprise architecture, but not necessarily practical skills of implementation. With that said, Monster.com lists typical responsibilities as: determining database structural requirements, defining physical structure and functional capabilities, security, backup, and recovery specifications, as well as installing, maintaining and optimizing database performance.

Creating and Enforcing the Rules of Data Flow. A well-architected system requires order. A Data Governance Manager organizes and streamlines how data is collected, stored, shared/accessed, secured and put to use. But don’t think of the role as a traffic cop–the rules of the road are there to not only prevent ‘accidents’, but also to ensure efficiency and value. The governance manager’s responsibilities include enforcing compliance, setting policies and standards, managing the lifecycle of data assets, and ensuring that data is secure, organized and able to be accessed by–and only by– appropriate users. By so doing, the data governance manager improves decision-making, eliminates redundancy, reduces risk of fines/lawsuits, ensures security of proprietary and confidential information, so the organization achieves maximum value (and minimum risk). The position implies at least a functional knowledge of databases and associated technologies, and a thorough knowledge of industry regulations (FINRA, HIPAA, etc.).

Making Use of the Data

We create a system in which data is well-organized and governed so that the business can make maximum use of it by informing day-to-day processes, and deriving insight from data analysts/scientists to improve efficiency or innovation.

Understand the past to guide future decisions. A Data Analyst performs statistical analysis and problem solving, taking organizational data and using it to facilitate better decisions on items ranging from product pricing to customer churn. This requires statistical skills, and critical thinking to draw supportable conclusions. An important part of the job is to make data palpable to the C-suite, so an effective analyst is also an effective communicator. MastersinScience.org refers to data analysts as “data scientists in training” and points out that the line between the roles are often blurred.

Data scientist–Modeling the Future. Data scientists combine advanced mathematical/statistical abilities with advanced programming abilities, including a knowledge of machine learning, and the ability to code in SQL, R, Python or Scala. A key differentiator is that where the Data Analyst primarily analyzes batch/historical data to detect past trends, the Data Scientist builds programs that predict future outcomes. Furthermore, data scientists are building machine learning models that continue to learn and refine their predictive ability as more data is collected.

Of course, as data becomes increasingly the currency of business, as it is predicted to, we expect to see more roles develop, and the ones just described evolve significantly. In fact, we haven’t even discussed one of a role that is now mandated by the EU’s GDPR initiative: The Chief Data Officer, or ‘CDO’.

Source: datasciencecentral.com


Describing Modern Data Management to my Mom

For Mother’s Day this year, I am inviting the women (and men) in my life to a belated celebratory dinner, as soon as I fly back to the Bay Area later this month. As someone whom I turn to for practical advice from time to time, my Mom rarely asks me to explain things to her. However, when I called her today to wish her a happy Mother’s Day, I not only thanked her for her unwavering support, but also described to her what the company I work for does, and why Modern Data Management matters.

Our conversation went something like this:

So Mom, you know how your cell phone provider charges you every month for the family plan, and sends you an email promotion every so often? Large organizations like them collect information about you and your family members to recall people, data usage and charges per phone number.

Different departments within the telephone company use different systems to save your information, such as your name and billing address. They also keep records of the times you called or sent them an email. Different departments, like order processing and customer support may store your information in different systems that do not talk to one another. For instance, should you call the customer support number on your bill, that department will collect the information that you give them in a different system than if you were to log into the online portal to manage your account.

Oftentimes, different systems have different information about you and your family members from different time periods. This is a problem because every department in the telephone company may have a piece of information about you, but none will have the complete picture. Do you remember the last time you ordered a cell phone online, and then called to change the color? It took the telephone company a while to locate your order. This problem will only get worse as they acquire more organizations, and add more systems and ways of communicating with you (email, phone, website, Facebook, Twitter, direct mail), because your information will be spread into even more systems. In order to provide consistent customer service, all communication channels and departments, like sales, marketing and customer support must have access to the same, consolidated customer information.

Organizations like telecommunications service providers and retailers use Reltio’s Modern Data Management technologies to build applications that provide a 360° view of customers like you. Our technology provides a way to connect to all systems from all departments, and create consolidated profiles with reliable customer information. This reliable information is then made available to all business and analytics applications used by all departments. Reliable information ensures a consistent customer experience across channels and departments, and builds trust and loyalty with customers. So, the next time you call them, you won’t have to repeat what you ordered or what type of data plan you have, depending on whether you use an iPhone or an Android device. They will have that information readily available. We also help organizations understand and manage relationships. Like Facebook, where people can connect to one another and follow their favorite stores and restaurants, we help organizations understand how people are related to each other, and to other organizations and locations through a graph. This helps organizations, like our cell phone provider understand our household needs.

Another reason why Reltio’s platform is modern, is because it uses your profile information to analyze whether your information is correct and complete. We use machine learning and predictive analytics for that. Machine learning make programs “smarter,” by allowing them to automatically learn from the data you provide. Predictive analytics takes information from existing data to find patterns and predict future outcomes and trends. Machine learning and predictive analytics form a recommendation system that recommends satellite TV service or a new phone accessory after learning about your tendencies and desires. These intelligent recommendations help people in marketing and sales determine which promotion to send you, on which channel (email, phone, USPS), at which time. That’s pretty much the gist of it. See you later this month!

At Reltio, our core mission goes beyond MDM. We are focused on simplifying all aspects of data management for IT, while delivering high value data-driven applications into the hands of frontline business users. Reltio Cloud achieves this by combining MDM with multichannel interaction data, predictive analytics and machine learning, all within a unified Modern Data Management platform that operates at Internet scale leveraging big data.

Source: reltio.com