Join Alan Simon for an in-depth discussion in this video Project roadmap, part of Manage Your Organization's Big Data Program.
- You'll be building the ordered list of specific big data projects over the next two to three years that when those projects are finished, they'll bring your organization into the big data era. Here's a good example of the big data roadmap beginning in year one. You've decided that your early release functionality will be streaming social media data into Hadoop as your big data proof of concept, and that you'll build a social media sentiment and passion dashboard with that data. Once you've accomplished those early stage projects, you turn your attention to consolidating your sales data marts into Hadoop.
And at the same time, using that consolidated data for a new generation of predictive analytics for your sales leadership and the overall sales organization. In Q3, you use that same consolidated sales data for a pilot program in your Hadoop sandbox where you'll experiment with advanced discovery oriented analytics to try to find undetected patterns and relationships within your sales data. And at the same time, your big data team will turn their attention to doing a first round of consolidating many different finance oriented data marts into Hadoop alongside the social media and sales data that's already there.
As you get to Q4, you connect your Hadoop, big data environment with your existing enterprise data warehouse as the new staging area and replace your existing ETL feeds into the enterprise data warehouse with those now coming from Hadoop. Your team continues their work with finance data mart consolidation, and then you turn your attention to big data support for IT by streaming web and network security logs into Hadoop. With the first year's worth of big data and analytics accomplishments on the record, you shift into the second year now of your roadmap.
Your roadmap calls for ingesting the massive amounts of data that your organization has in SAP. And you start by using a combination of streaming and batch data feeds to go after your North America ERP data. But at the same time, your team continues going after IT data as they began doing during Q4 of the first year. Now, they're going to bring in performance data from your servers and systems. In Q2, you continue your ingestion of SAP data now focusing on your CRM data. And in concert with that, you start building out your next generation of CRM campaign management capabilities now fueled by big data and advanced analytics.
In Q3, now that you have your SAP North America ERP data already in Hadoop, your core team will work with the analytics specialists on your extended team to build new, predictive and discovery analytics for HR. And then, at the same time, your data specialists start ingesting data from your outside business partners, specifically third party manufacturers and third party logistics and transportation companies. You then close out year two of your roadmap by building a dashboard for your chief information officer and the program management office that delivers predictive analytics about your IT programs and projects, including your own big data program, but also, all the others around your enterprise.
Your team will also build another set of predictive analytics. This one related to production line performance since you have your SAP_ERP data already into Hadoop. And then, at the same time, another part of your team will enhance the social media analytics that were already built and now deliver a second wave of those capabilities. As you can tell from years one and two, your roadmap will have a steady progression from the very beginning with both user facing analytics as well as the ingestion of data into your Hadoop big data environment.
Interested in leading the charge? Manage Your Organization's Big Data Program is for business intelligence professionals who are tasked with implementing a big data and analytics program at their companies. Alan Simon explains the role of program managers, their desired skills, and the people they need on their core team. After the preliminary steps—defining program direction, budget, and initial projects—Alan helps map project milestones and define the KPIs to help track their progress. In chapter 6, you survey the risks to the program, from financial and technological standpoints. By the end of the course, you'll have the skills to be successful in a big data leadership role—and drive data-driven insights throughout the enterprise.
- What makes a good big-data program manager
- How to recruit members of a big data team
- Defining the program direction
- Creating an initial list of projects
- Identifying allies and adversaries
- Determining program milestones
- Managing milestone progress
- Assessing program risks