10 Strategy Strategies for Building Data Plans for the Companies

Practices in management need to change to bring together established transaction structures and contemporary analytics capacities. The term "contemporary analytics" refers to predictive analytics (PA) and modern decision-making (DM) systems that, when leveraged with existing recording systems, form a coherent, agile and new operating model, which we refer to as intelligence systems. Intelligence systems, we believe, will form the foundation of true data-driven organizations and offer considerable competitive advantage to organizations that possess the requisite processes, expertise and technological expertise to bring these systems to life.

The following parameters are fundamental to this capability:

Established recording systems (e.g. transaction systems) must be leveraged and developed by introducing modern analytical techniques, processes and technologies to expand these systems; such systems must be capable of managing many, diverse data driven sources and mixing them into a coherent data model; analytics must be operational and incorporated into recording systems such that both humans and m are capable of mixing them into a coherent data model;

Importantly, Intelligence Systems are not a tailor-made analytics tool, operated by data scientists and business lines. Instead, they reflect a fundamental transformation of current recording systems, applying agile techniques, new tooling, and automation to help data-driven cultures and impact business results in near real time.

10 Strategic Steps to Implement Systems of Intelligence

1. Keep core recording systems on current platform(s):

Understand the cost, elapsed time and risk of moving core systems from one platform to another is very high, and strongly resist calls for moving recording systems in whole or in parts; ensure that other technology decisions do not compromise the ability to incorporate inline predictive analytics; plan for predictive analysis;

2. Develop current recording systems infrastructure:

Move to 100% all-flash storage to increase IO times and the IO processing time, plan for additional IO load from predictive analytical analysis; move to True Private to ensure efficient recording equipment system and support costs;

3. Improve Software Application Developers System productivity:

Allow even faster data driven access by publishing full copies of production databases and code bases from shared flash data to increase programmer productivity, shorten development cycles and boost code quality

4. Place Analysts / Data Scientists Next to the Business Line:

Introduce a clear incentive program for managers and practitioners to switch from intuition to inline predictive analytics models and algorithms that can be applied within the Record Systems; Maintain a positive and cooperative partnership between analysts, data scientists and record development systems with specific incentive plans to reward them.

5. Understand Possible Data Sources inside and outside Organization:

Data from the Internet of Things; Data from the social, mobile and industrial Internet; Understanding the possible for incorporating sensors and security / video feeds within the enterprise (warehouses, vehicles, etc.); Understanding the potential availability of data from data aggregators (e.g., supply chain data inside an industry).

6. Place Location of Data Sources to Reduce Cost and Elapsed-time to transfer Data:

Ensure that all relevant data driven sources are available and can be transferred quickly enough to use in real-time inline analytics; investigate potentially positioning recording systems in a mega-datacenter near cloud resources and external data sources if or when external data sources become a cost or time problem.

7. Provide best-of-breed analytical tools to analysts / data scientists:

Allow on-site and cloud-based analytical tools and applications and all data sources to be accessed; training to move insight to inline analytics systems to be applied on recording systems.

8. Encourage improvements

From basic scoring models to clustering models, decision trees, neural network models, naïve Bayesian classifiers, random forest models, etc.; introduce a clear incentive program to switch from personal intuition systems to inline predictive analytics and algorithms that can be applied within recording systems;

9. Implementing standards

To enable direct transition from model development to Strong incentive data driven program to minimize the time-to-value of new and enhanced analytical algorithms from months to days / hours

10. Gain traction

From one business process in a single business line and grow quickly to include other business processes and additional business lines. Executives should be especially vigilant to ensure that senior executives do not hijack data models for their own use, and should ensure that programs to boost data driven attributes are supported and completed to simplify business processes.

Marketing experts believe these moves are a requirement for a successful return on investments in big data and analytics. The use of data driven technology portfolio as a reference model for this business case to construct support of the processes mainframes reflect the classic gold standard for large recording systems.