Much Ado About Data in 2018

As we usher in 2018, organizations are faced with new challenges but also many of the same that come year after year.  What we didn’t get to in 2017 still stares us in the face.  Was data strategy one of those initiatives that your organization planned to get to eventually?  Not knowing where to begin is the biggest reason that we hear from organizations of all sizes.  In response, we created what we call Data Process Innovation, a service that connects the dots between specific business requirements and data-driven solutions.

We take a holistic approach that simultaneously addresses people, process, and technology. By starting with business objectives and understanding the different software tools and processes that support current tasks, we are able to identify addressable gaps necessary to transform and unify data to support reporting or advanced analysis.

Data Process Innovation consists of five steps:

1. Business Process Mapping. Explore your specific business processes and how they impact your organization’s business objectives and goals. We interview stakeholders to understand how and when data products such as reports, analyses, or recommendations are leveraged to accomplish business objectives. We also develop an understanding of business objectives stakeholders would like to accomplish with data, but traditionally have been limited.

2. Data Awareness. Understand how your data can improve business processes and strategy across your organization. At this stage, we shadow professionals responsible for preparing specific reports. This gives insight into situations when execution differs from the documented process and often reveals insight into hidden challenges within data and systems.

3.Data Readiness. Assess your data environment for reporting and analytics capabilities. With the help of IT stakeholders, we access the data resources that support the business goals documented earlier and assess data quality. This includes consistency of format, completeness of information, and accuracy. This step helps identify any transformations or quality assurance processes that need to be developed.

4. Modeling and Analysis. Design custom data science solutions to complement your business objectives and current state of technology. With a better appreciation for both processes and underlying data quality, we are able to identify the most appropriate analysis and machine learning techniques to meet our clients’ goals. This includes the use of third party tools and API’s that are included as part of a data analysis pipeline. We map out the expected inputs and outputs for each step, and design tests to validate and measure the accuracy of any outputs.

5. Solution Roadmap. Develop a plan and timeline to deploy data science solutions that integrate with your organization’s processes and technology. At this stage, we identify any new databases or software requirements that need to be implemented to support the mapped out machine data science solution. We work with clients to identify workloads that are appropriate for them to address internally and opportunities for our data scientists to assist with execution.

There is no need to tackle this alone. The Pandata team will be with you through the entire process, making it painless. Let us help you become proactive with your data.  There is too much it can tell you to push it off yet another year.  

To learn more, visit us at pandata.co or drop us a line at hello@pandata.co.

 

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