No failure: Common analytics project challenges learning

By ridhigrg |Email | Dec 14, 2018 | 3360 Views

There is no organization which starts data management having already planned that there is no success. Failure is when they do not meet the expectation and do not implement solutions or not giving their highest probability. Through failure, people lose confidence for the teams potential and business intelligence trust is lost for data and profit. The future use of business intelligence can be affected if the project's value is not maximized. Therefore, the successful project implementation value should be understood and how it is adopted, and staying away from the drawbacks can help you realize the value of data investment. 

Common challenges that every organization face are:
Time to implementation vs. time to value:
At the time of planning projects, implementation time is the key project which is the last point of the development cycle. To ensure that solutions are delivered timely the main focus is on gathering the business requirements. While developing the business intelligence applications, it should be examined that what will be the time for the development of the tool and timeline should be leveraged for success, considering the new platform adoption, process of data management and development of the application. At the time of delivering solutions you may signal the end of design to end users and the delivering time, as it does not lead to value which is automatic. Understanding the proposition of the value of the data which is accessed and the visibility which easily affects the business decisions helps you to build the gap between the time to implementation and time to value.
 
For future requirements you need current evaluation:
The current needs of the project should match the requirements of business intelligence, and the future requirements should also be understood, and for expanding and scaling the initiatives you need to build a roadmap also. Departments often implement their own solutions, which result in dashboards multitude for meeting the needs of the teams. Therefore, it's not possible that all the needs can be anticipated but some points like the additional data which might be captured in future, who will access analytics and some cases which will be required to define the growth. The future requirement map may help your organization in making the selection for the right software and also prevent the revaluation which is costly of their toolsets.
 
Incorporating people and process into technology:
Business intelligence is too important for organizations because decision making cant be separated from business operations. There are technical requirements also on which IT teams focus like the accurate data which is needed, how data complexities are being handled and the required metrics. But the business information needs that information which might help them. Understanding and then absorbing helps in the development support and adoption of higher level which should not be overlooked. Many businesses mainly focus on the decision making with the adoption of analytics which helps to increase the understanding of why to incorporate these areas. 

The complexities of strong analytics:
Outcomes which are successful they need the desired goals and the way o reach them. And this path includes linking with people, and technology to look upon the further outcomes and ensures that analytics and operations are tied up. Initiatives which are strategic must have an environment which is developed to make sure that analytics are not reliable, but to make business decisions better it should be proactively delivered. 

Source: HOB