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The Reasons Why Business Partner Concepts Fails And The Back-Up Plan To Overcome From Them

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Introduction

Business Analytics is offering a set of recommendation to derive the insights from information and then implemented through set of actions to better prospects. It can be done through planning, costing, business intelligence and forecasting about the project or company’s overall conditions to understand the scenario they need to ask certain set of question such as what is plan, why we are doing it, how we are doing it, what will happen after application of the plan. Business Partner in analytics is not an individual. It is an institution who has set of solutions for the customer for their analytical difficulties. They are forecasting customer’s opportunities to get timely, insight precisely about the data. They can use machine learning and advance analytics potentials in the business process to bring better results.

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Now the organization are closely working the Business analytical partners understand the business or project wise awareness through customer loyalty program analysis, prediction modelling, cost optimization analysis, cost-marketing analysis and sales forecasting etc.

Analysis

  1. Lack of Planning and Execution
  2. To get the insightful examination resources and deal with data which gather till now require a set of course of action or an analyse the data directly. If the inter-departments are lacking for the required skill to perform the task or doesn’t carry out the proper execution, then it is difficult for the organisation then they will look for outsiders who can perform or contribute towards assorted various activity.

  3. Targeting on big data instead of correct data
  4. Most of the companies are targeting towards something big or large. Instead of that if they started evolve the right data or accurate data which is required for the business or the organisation then it is more valuable. 99% we get the valuable insight from the small data set. So now it is the right time when we can focus right/correct data instead of large/big data then only we can ask the precise question.

  5. Communication issue within organisation
  6. While working on the project, there should be a two-way communication between data modellers and top executives. Any company is following the two-way correspondence to avoiding the unachievable expectation, unnecessary costly implementation and unimaginable circumstances happen to due to which the downtime arises in the company or for the project which will ended up with these kinds of scenario. Sometime company should fight back Technological; personnel and management issue is more better to improve the market condition while taking analytical activity on primary basis.

  7. Clarity issue
  8. The organisations who dealing number of projects within onefinancial year. Now-a-days organisations are also dealing the large number of data which is not completely as well correct and not lined-up with the company’s objectives, line of action, policy and procedures. So, it must be very specific in terms of business goal to maximum benefit from the project. This is happening due to lack of clarity about project and investment which you have invested in the project. For these company need to understand for which project the exact analytical tool to be used with the right kind of human resource. This is utmost vital when the large amount data would be considering as main factor to make the decision.

  9. Lack of human resource
  10. Companies are lacking behind due to lack of skilled human resource. It means any company is suffering from human resource shortage who are easily or perfectly performing the analytical projects. Now the Analytics field upcoming area in every kind of industry such as if it a retail, finance, bank, healthcare etc. so the demand is more for the skilled labour in this industry is more for who know tableau, Power BI, qlikview etc. but the supply means who know the is very less.

  11. Data Silos
  12. Data silo is a place where the fixed data is stored within the organisation’s data warehouses which can be access by any department for the internal purpose. This term is mostly heard in big organisations or Multi-national companies. Where the internal – departments are competing for their project goals instead of working together to achieve organisational goals and objectives.

    Recently companies are facing some issues not because of productivity but also it is badly affecting the organisation’s data integrity policy. Data silos is the after effects of the technology that restrict the co-ordinated efforts of the company’s overall business. We can overcome from this issue with the help of cloud back-up is option for data silos. This can be stored in only one single archive instead of number different data silos.

  13. Data Inconsistent
  14. The organisation is frequently facing data inconsistency issue which is also known as dirty data that means misspelling word or number, empty fields in the excel or another analytical tool. This is one common and basic hygiene issue in any company. these kinds of issue happen within the department in most cases. Suppose how to find the dirtiness of the data in healthcare for e. g. drug description is incomplete not in format, drug with missing the unit of measure, drug vendor name is missing or his catalog number etc.

    The Analytical team or the analyst must critically evaluate the data to remove the dirtiness or the garbage in the data. For they need to implement the data cleaning approach at the initial stages which is also know data wrangling.

  15. No True ROI
  16. Most of the organisations are committing promises about the profitability in the project outcomes, but they never think about that the profitability depends on costing and accounting methodology of that project. The ROI is just not only depending on software and related tools but also human resource required, time taken for the project and health understanding with the customer. In the initial stages, we can’t figure it out the accurate ROI for every project. It may vary as the time as the process of the project starts. So, it is unpredictable.

  17. Number of KPI’s
  18. Key performance indicators are crucial indicators for any project which evaluate it goes in right direction or not. Or we need some corrections. Too many performance measurement parameters to evaluate the process is require more attention, analysis. For these company need to restrict them down on time, work factor and eliminating confusion in the data which organisation have stored. So, that deliver the exact information with high level of accuracy factor.

Conclusion

Organisations are updating themselves with the Big data analytics and upcoming Business intelligence techniques which are more closely observing on the challenges and preparing themselves to optimize the total cost to value factor within the project to become successful in future. I will insist organisation to avoid the common loops to work effectively and efficiently on their analytics tools to incorporate with big data.

15 April 2020

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