The Path to Data and Analytics Modernization

Editor's Notes

1. Everyone seems to be talking about data modernization these days. It’s no surprise, as the old ways of approaching data and analytics are not keeping up with the sorts of technology demands we’re seeing today. Organizations have: Too much data, too many integrations Organizations can’t keep up with volumes and varieties of data with ingestion, integration or cataloging. There are different types of data vs just relational sources. Older analytics systems have trouble playing nicely with semi- and unstructured sources Reporting, dashboarding, and Excel are no longer sufficient toolsets - streaming, real-time, near-real time, embedded use cases, advanced analytics, and bi-directional use cases are growing. Architectures are getting more complex. Data sources have been on-prem and in the cloud for a while now, but now organizations now have source systems, data warehouses, operational systems, etc split between being hosted on-prem, in the cloud, in multiple clouds, or some combination of these. Scarcity of talent – It’s hard to find people to maintain your old stack, let alone keep up with the rapidly changing demands in the marketplace. Organizations are having trouble finding the right people to bring onboard to help.
2. TONY You may have heard these technical demands through the business perspective instead. They really get at the same problems demanding modernization. To stay competitive in today’s market, companies must be able to use their data to make better decisions and respond to the needs of a dynamic business environment.  Take advantage of the explosion of data: Organizations see the competitive advantages with the ability to access, use, store, transform, and analyze more data- including increasing data volumes, types, and sources. There is a need to move quickly to beat the competition to market or at least in the race of information. The number and variety of data sources is growing constantly. Innovations with social media, different channels, customer engagement, marketing, external data, etc. are forcing businesses to adopt more and more technologies to keep pace. The number and volume of data sources requiring integration are constantly increasing, making it difficult to keep pace. Need high quality data and want real time information: The business demands more and better analytics- which require quick and easy access to all data that is up-to-date. Have the ability to do advanced analytics- now or in the future: Companies at a minimum need the foundation in place to perform advanced analytics like machine learning and AI. More people throughout the company want to get their hands on data: Analysis isn’t just happening in IT anymore- all departments and functions are becoming data literate. People throughout the organization want the ability to slide and dice the data.
3. TONY The old way of doing things isn’t keeping up. It requires modern thinking and modern strategy to adapt to your modern data problems. TONY
4. TONY So what do I mean when I say “data and analytics modernization” First - Modernizing data and analytics is not about a single action or implementing some suite of tools. It is rethinking how you use data and analytics as a company.  Oftentimes people characterize “modernization” as just moving something to the cloud; but the approach you take and the advantages you realize go beyond just cloud adoption. These solutions expose advanced analytical capabilities that help you make smarter decisions. Modernization is leveraging modern data management principles. Yes, it involves moving from legacy databases and architectures to modern, cloud-based platforms and scalable architectures. Modernization is moving from legacy, traditional BI tools to modern, next gen analytics tools to realize returns on your investments in data. You do this by delivering better and faster analytics, including augmented analytics and machine learning. What sorts of returns on investment? What are the benefits?
5. TONY When you adopt a modern approach to data and analytics, you realize all sorts of benefits. All the benefits of moving to the cloud- Speed, scale, flexibility, rapid prototyping, lower TCO, ​etc Provides better management of ALL data- to process the variety of data available Support faster data access and integration of new data sources and types- with a focus on rising data volumes and use of multiple data sources Reduce time to insight, giving users the ability to quickly find value in data, as well as ingest streaming data to analyze events as they unfold. Democratizing access with data stored in one place for every business function, thereby empowering data analysts to run analytics without acquiring new skills. Planning for the future- having the foundation in place for more advanced types of analytics, such as artificial intelligence and machine learning. Provide better analytics and rapid reporting- which enables real-time decision making- ability to quickly access all data, pull in new sources, analyze changes
6. TONY How do you get there? You need a balanced approach. We’ll touch on each of these points today.
7. KEVIN
8. TONY Your data and analytics modernization initiative should be viewed as a high-stakes project driven by a long-term strategy. Address if you do have data strategy already, need to reexamine it. Make sure to connect data strategy to modernization.
9. I’m not convinced we need this slide TONY Technology does play a part in the data strategy though. Choose the tech that’s best for your organization based on your goals and on your requirements Consider your current data needs, and choose an approach to data management and data architecture that can expand with your needs over time
10. Start with principles before moving to architecture
11. Look familiar?
12. 1. Power BI 2. Looker 3. Qlik 4. AWS QuickSight
13. 1. Power BI 2. Looker 3. Qlik 4. AWS QuickSight
14. KEVIN
15. KEVIVN
16. KEVIN
17. KEVIN
18. 1. A training and enablement plan in place to mitigate the learning curve on the new tool 2. How will they receive training: can you develop a strong training program internally ,or do your require outside help to initiative 3. Load balancing. This isn't just a process of learning a new tool. It’s a process of maintaining your legacy tool, and translating those reports to your new tool. How do you divide the workload in your team? Do you need outside vendor help? Who takes what aspect to the process? 4. Don’t just lift and shift- don’t replicate your old analytics solution… Often the analytics selection process takes on such precedence that the question of “who” gets obscured. Your people must be factored into decision-making because they will ultimately be responsible for building and developing reports. A modernization project isn’t just a shift in technology; it’s also a shift in the skillsets required within your organization. If your team of developers must pivot from their legacy platform to a modern analytics solution, they need an enablement plan to mitigate the learning curve. Consider how they’ll receive training too—can you develop a strong training program internally, or do you require outside help initiate? Not just a replication of your old analytics solution--- don’t just take what you know. Use it as an opp to improve the app- how are the dashboards being used? Could they be improved?
19. 1. A training and enablement plan in place to mitigate the learning curve on the new tool 2. How will they receive training: can you develop a strong training program internally ,or do your require outside help to initiative 3. Load balancing. This isn't just a process of learning a new tool. It’s a process of maintaining your legacy tool, and translating those reports to your new tool. How do you divide the workload in your team? Do you need outside vendor help? Who takes what aspect to the process? 4. Don’t just lift and shift- don’t replicate your old analytics solution… Often the analytics selection process takes on such precedence that the question of “who” gets obscured. Your people must be factored into decision-making because they will ultimately be responsible for building and developing reports. A modernization project isn’t just a shift in technology; it’s also a shift in the skillsets required within your organization. If your team of developers must pivot from their legacy platform to a modern analytics solution, they need an enablement plan to mitigate the learning curve. Consider how they’ll receive training too—can you develop a strong training program internally, or do you require outside help initiate? Not just a replication of your old analytics solution--- don’t just take what you know. Use it as an opp to improve the app- how are the dashboards being used? Could they be improved?
20. People and processes- critically important- when modernizing technologies, need to ensure people are trained (look at blog post KL) Important to manage data literacy- ensure users are trained/ can do analysis on their own Data democratization Allows companies to make data part of their culture instead of just another task Must have a data strategy- you can’t just implement a modern tool and now you’re modern- you need to have a strategic approach A program not a project- should be ongoing activities Is there a lower barrier to entry for certain technologies- new capabilities that people can take advantage of ( If you want to take advantage of more advanced techniques, you need to have a modern foundation in place You cannot just carry your legacy problems forward. How do you justify the spend? Identifying places where you can identify ROI for modernizing Include summary points for data management section (modularity / agility, not following crowd (fomo))
21. Analytics Tool Assessment (Applicable only if tool has not been selected) Analytics8 will review strengths and weaknesses of modern analytics platforms, and what is the optimal fit within a client's upstream data architecture  Analytics Bake-off (Applicable only if tool has not been selected) Analytics8 will build proof-of-concept applications in the selected Analytics tools. The goal is to utilize a common data set across all analytics tools to contrast feature functionality POC Build (Applicable when a tool has already been selected) Analytics8 will help the client define a Proof-of-Concept project in the selected Analytics tool. Typically this entails taking elements of an existing legacy analytics report, and migrating it over to the specified Analytics tool of choice. The goal of this exercise is to understand feature functionality of the new analytics tool, and how user experience can be constructed to find commonalities back to the legacy analytics platform Roadmap Planning Once POC is complete, Analytics8 will conduct roadmap planning to create a migration path to transition reports to the new analytics tool and retire reports that are no longer necessary Analytics will prioritize and build use cases for advanced analytics, data science and augmented analytics Production Build Analytics8 will focus on building operationalized reporting within the new analytics tool to roll out to end users Adoption and End User Training Customized classroom / virtual training on Analytics platform Boot camp training on production applications to drive user visibility and adoption