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  • admin
  • 17th Aug 2020
Technology analysts claim that “A Decade from now, the people who are not embracing Data analytics in their business processes, either would be retired or would be out of business.”
We have come to a situation where the companies are under immense need to start their Data Analytics journey to stay relevant in business. Now is the right time to start this due to the following reasons,
  • The cost of necessary infrastructure is reducing day by day and the capability of hardware is increasing proportionally
  • The Analysts with right skills are abundant in market which makes the formation of a skillful team easy than ever before. You can engage a partner team specifically for this project instead of investing on a full time team initially
  • The analytical platforms needed to process the huge data are now available on need to use basis, no need to invest heavily, we can pay as we use.
Given these favorable environment, the big data voyage always starts with the leadership and their vision to take data driven decisions while moving company towards strategic milestone targets. Once the Leadership has decided to steer the company ahead using insights from Data Analytics, the next question asked is “what are the next steps? Where to Start?”
The Steps to follow to set your organization in motion to become a Data driven decision making company is below,
  • Decide on the right data architecture, align it with Strategy: what kind of data need to be collected to move the company towards a desired goal? Which processes are we targeting for analysis?
  • Proper Data Governance: How are we collecting and maintaining the data (Process data, Production line data using sensors, Equipment and Machines data or Usage data of end products via IoT), Access to necessary data without hassle for the necessary teams, What analysis do you plan to carry out aligning to your KPIs?
  • Assembling of right mix of team, each team member to be well versed in analytical tools along with clear business perspective.
  • A Pilot project, targeting a critical area that needs improvement, to test the Analytics outcome, team effectiveness and to set direction for further projects
  • A companywide expansion plan once the pilot project is proven fruitful
  • Integrate Data analytics with all core business value streams, so that a high level direction is achieved by doing multiple smaller projects across each value stream.
Key Features that are necessary while deciding on the Big Data platforms to carry out the analysis is below
  • Ease of Access: The team members across the organization should have necessary and needed access to the data needed to perform analysis without having to reach out to IT every time for access. This helps is avoiding the piece meal data analysis from happening.
  • Expandability: The volume of expansion is unclear for the big data projects during initial stage, so the IT platform we choose must be having options to scale up to any size we expect in immediate future, any platform which restricts us from operating to our flexibility is not suitable.
  • Early insights: Apart from Data capturing and analyzing capability, our capability to predict a scenario and act on it to use it to our company’s advantage plays a vital role in real time. So the platform we decide on should possess the capability to support Machine learning and Artificial intelligence that builds the predictive capability to our analytics.
  • Integrated: The ability of platform to integrate with multiple environments we have in each value stream also plays a key role in analytics. It should enable us to access data effectively from across data pools and help is carrying out fast paced analysis.
Your partner should have capability to collect data from the Shop-floor using suitable technology, and to monitor the KPI of the manufacturing unit by bringing them together in a dash board for your leadership team to keep a tab on department / unit specific KPIs. This will help you to take real-time data driven decisions based on the current performance of the plant.
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