Post-large data age: from it to the user-oriented role transition

The implementation of the "TechTarget China original" Large data solution has put many IT departments in a dilemma. Large data applications do not require the same infrastructure as the support team does for applications. As companies accept big data, management assumes that the size of their workforce will be reduced. And what do non-essential technicians do? One answer is to transform it into a technical advisor and collaborate with the business unit. In other words, it's giving them a customer-facing role.

the status of information technology after large data

The Big data revolution is over, allowing information technology (IT) companies to adopt new technologies to varying degrees. Some fully embraced the concept of large data storage and complex analysis, and adopted best practices and proven project management methodologies. The result: A treasury of analysis of products, sales, orders and most important customers.

Other users are less fortunate. Perhaps the specific vendor solution they purchased does not include a complete plan, or perhaps middle managers are not ready to devote the necessary mature resources to a successful solution. The result is a semi-structured collection of information, a handful of analytic solutions, and the idea that one day in some way or another will pay the price.

At the same time, database administrators, System programmers, and other technical experts and subject matter experts are in a post-large data age. Hardware and software installation and configuration has been completed, proof-of-concept and pilot procedures have been delivered to the first internal customers. Most hardware and software mixed data solutions do not require internal tuning: Analysis queries execute very quickly, and business analysts are looking for useful information to explore and investigate.

What's left for the technicians to do?

Current Infrastructure Team

First, let's consider generalists. Novice DBAs and System programmers start in this category simply by developing professional skills and deepening knowledge in their careers. Their best roles are often well-defined, standardized processes. These include:

manages database backup and recovery processes, monitors and detects disaster recovery preparedness, and implements self analysis and self tuning processes (sometimes called autonomy); SQL query tuning, including explains and access path analysis, database Performance tuning, test production objects and data migration, monitoring database logs for errors and problems.

In a new environment, many of these processes are redundant or unnecessary. Given the speed of large data analysis solutions, SQL query tuning may not be necessary in most cases. This is also true for database performance tuning, as many vendors ' software and hardware hybrid solutions (sometimes referred to as devices) are delivered under functional conditions that do not have any internal performance tuning. This makes it easier for the support staff to complete the work.

The result: there is no longer a need for generalists in IT companies in the post-big data age.

Next, let's consider the experts. They specialize in dealing with complex tasks:

system and network performance tuning, software installation and release migration, primary technical support for mission critical applications, assistance and management of data schema changes, benchmarking of potential vendor tools.

As it companies accept large data analysis, fewer experts will be required to do so, which means less demand for experts.

Infrastructure support in the post-large data age

IT pros and generalists must shift their attention to internal users, who face many technical problems. Here are some of their most pressing problems.

Large data performance. As the value of large data applications grows, the use of analytics by business analysts increases dramatically. More queries produce operable results and generate regular reports. Users will require more time spans and greater geographic area data. Eventually, a large number of users and queries will overwhelm your large data application.

Experts should be aware of increasing the knowledge of internal user applications. What data do they need, when they need it, and who will use the results? Experts change their value by becoming subject matter experts in some application areas, serving as internal consultants, and consulting with advanced analytics methods such as query efficiency and similar datasets.

Generalists can provide valuable services in their environment by collecting performance data and using statistical information. This data may be used to prioritize the query categories. For example, low priority queries defer processing when resource utilization is low.

Enterprise data Model. With more and more data stored, having an organized data dictionary and data model becomes particularly important. If you do not know what kind of information you have, how can we effectively conduct inquiries?

Experts, especially database administrators, should be aware of the concept of data modeling and should have knowledge of multiple applications and systems. This knowledge can now be leveraged to assist business analysts with initial analysis definitions and query builds.

Generalists can serve as application investigators, provide a list of data elements, and classify and validate data attributes and sources.

Large data technology. Big data is not just about user names, accounts, and deposits. While these common business data can be included in large data solutions, there are more interesting data elements and data types to explore:

There are also new types of complex data, such as large objects (lobs).

Self-descriptive data such as Extensible Markup Language (XML) is becoming the de facto standard for internal application data transfer. Many documents and data records are stored in XML format because they can be read through multiple cross-platform applications.

Multiple-structured data is common when capturing user site behavior. The so-called click Stream provides a way to track what site visitors do and what data they visit, and their preference data.

Some of these new data types can be confusing to business analysts. Experts and generalists can provide services for reference and answer questions as well as for presentation of new data types and how best to use them.

Managing Transitions

Support managers should find a relatively easy way to help their employees with their transition.

Some generalists will initially serve specific internal users to serve them in the definition of data requirements. This can then be extended to more technical roles, including analytical execution or result analysis. Even transferring generalists to lines of business is worth considering.

Many experts continue to deal with technology-related issues, such as performance and tuning. Some provide in-house advice on advanced analysis options, methods, and analysis of new data types. Management must keep these experts active at work, or else risk losing them to other similar businesses.

Summary

The infrastructure support team successfully underwent large data baptism and was most likely responsible for the successful implementation of many applications. However, this result leads to an unavoidable reduction in the demand for IT support personnel. IT pros and generalists must extend their skills by learning the internal lines of business and letting them familiarize themselves with current business data requirements. Suppose a user-oriented persona may be their only long-term career choice.

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