While organizations are looking to gain insights from their most valuable data sources, one roadblock that often gets in the way is timing. In the past, the tasks to compile and analyze company data was a labor-intensive process. Our webinar, hosted by the Richmond Technology Council, focuses on how Impact Makers helps organizations incorporate better, faster data science for data warehousing to provide the accurate, real-time analytics that deliver the most value to their business.
ShareBy: Mike Brennan, Lead Consultant, Impact Makers and Kevin Cox, AWS SSA, CCSK, Lead Consultant, Impact Makers This is the…
ShareBy: Kevin Cox, AWS SSA, CCSK, Lead Consultant, Impact Makers This is the second post in a series about sharing…
ShareBy: Kevin Cox, AWS SSA, CCSK, Lead Consultant, Impact Makers Why would providers, payers, and consumers share Healthcare data? Healthcare as an industry…
Without a doubt the advantages of migration to cloud computing and operational adoption have been a foundational game-changer for large organizations. However, cloud computing is fast becoming a change agent for Mid-Tier Enterprises (MTEs) as well.
Are you a business leader trying to mature an enterprise drowning in data? Perhaps you’re a data leader looking to harness the untapped potential that lies within your company data. We’ve helped many clients on these journeys and have resources to show how a modern data ecosystem can deliver business value.
It’s great to see that a team at AWS has made COVID-19 datasets readily available to the public (read here for detailed information). The datasets are accessible from a public S3 bucket to anyone with an AWS account. For healthcare providers and payers that want to begin or enhance their analytical efforts around the coronavirus crisis, the COVID-19 data lake can be a useful accelerator.
Robotic Process Automation – using bots instead of associates to perform processing tasks – can be an effective way to increase speed and accuracy while redeploying associates on higher-value processes and projects. RPA implementations are commonplace across financial services and healthcare, as both industries have a disparate mix of non-integrated systems and data-intensive processes. However, RPA as a solution has finite value and should be compared against more strategic solutions such as true machine learning, BPMS and microservices integration.
Across all industries, the race is on for firms to differentiate with data — through data-driven products; enhanced customer acquisition and experience; reduced risk; or streamlined operations (cost-out). C-suite executives have the aspiration and vision to win with data-driven insights, yet most are dissatisfied with the cycles consumed in producing data-driven insights. The time it takes for a business to deliver a quantitative insight from when an internal stakeholder or external customer needs it — let’s call that a firm’s Data ID, short for Data Insight Delay.
Data Storytelling is an essential skill of of any data scientist that makes data come alive. It is a structured method for turning data insights into action through analysis, design, and narrative. It is an art that brings interpretation and clarity while engaging the audience towards action. The Framework defines the art of data storytelling, as well as provides guidelines for how to do it successfully.
Data is something everyone uses and needs to do their job. When people don’t trust their data, organizations have a big problem on their hands and it won’t go away overnight.
The only way to fix a lack of trust is to build trust. Getting employees to buy in to a new way of using data is a process of building trust. Just taking the spreadsheets away won’t work. People are more dedicated to their culture than any strategy.
While there are very tactical and very specific steps to take, it starts with the approach: understanding that data is a business problem, not an IT problem. From there we can focus in, all the way from brand, mission, and operating model, down to the key performance indicators to measure success. This is a process that takes time. Many organizations desire to follow a business-driven approach to data, but find quickly (and surprisingly) they are still treating it as an IT problem and they have a tougher time measuring success than they thought. This is the vital work we love to partner in.