Across all the industries and geographies that BlueGranite serves, today’s biggest analytics trends revolve around how to make the best possible uses of increasing amounts of data as cost-effectively as possible. The challenges of gathering, organizing and storing large datasets are exacerbated by the need to secure them while simultaneously identifying and delivering timely insights. And let’s not forget the need to do all of this while yielding more value than these capabilities cost to design, implement and support.
The analytics market is hot right now. The seemingly simple and universal goal of producing favorable ROI results often get obscured by the never-ending march of industry buzzwords and new product and service names. I am going to try and demystify some of the trending concepts that many of our clients have been asking about – offering some quick insights on what they are and how to use them, as well as helpful links to additional information that you can explore.
- Cloud vs. On-Premises Analytics Capabilities: What are some reasons why I should consider cloud-based analytics capabilities in conjunction with (or instead of) traditional on-premises solutions? Pay only for services being used, while they are being used, rather than paying up-front for all the capabilities of a software suite, whether or not you plan to use every feature. Eliminate long-term capex commitments by using flexible opex instead, which can be dropped or restarted on demand.
The list of pros and cons can go on and on, but it ultimately will depend on your business needs. Check out this blog post on why thinking cloud first could benefit you in the long run.
- Cloud-based Data Exploration and Visualization: What is the Power BI service, and what potential benefits does it offer over competing cloud and/or on-premises capabilities?
- Appeal of an inexpensive yet robust capability that is stable yet always upgrading (monthly desktop releases, more frequent server releases).
- Native support is available for Mobile BI on iPhone.
Some of our clients who looked at Power BI two years ago are surprised when they see how much it has evolved since then. Notably, Power BI was recently included in the upper-right Leader’s portion of Gartner’s Magic Quadrant.
- Embedded BI: How can I embed the insights from my analytics engine at the point where they are needed in decision making? Seamlessly delivering information via embedded BI enables you to trigger and inform good decisions within the natural flow of your key processes. If this is something you are struggling with, check out this blog post on Embedding Power BI Reports into Your Applications which describes ways to do this within Power BI and custom applications.
For example, the process can direct a manufacturing shop floor system to adjust an output setting based on sensors detecting an anomaly on the production line. It can even give coaches and trainers real-time guidance on how to help athletes adjust their performance to avoid injury based on data streaming from wearable sensors.
- The Internet of Things: The last statement above are just two examples, but much of the growing raw quantity of data being generated today is coming from distinct data sources that aren’t human. The Internet of Things not only includes data sources (such as streaming sensors, point-of-sale systems, manufacturing shop floor machines, and medical devices), but it also includes automated data consumers that remain online and hungry for data on a 24/7 basis, ready for an inbound alert or trigger from a sensor, or perhaps from sets of sensors that reach an actionable threshold defined via machine learning.
- Machine Learning and Data Science: What is R and how can I use it for machine learning and data science that makes a difference? R is an open-source programming language that supports computational statistics, visualization and data science. R is also used for statistical programming, developing and training models to identify cause-and-effect relationships that support predictive analytics. And if that wasn’t enough, R is now supported on SQL Server 2016 and Azure via R Services, and on the standalone R Server platform.
- BI and Analytics Governance: What do I keep hearing about governance, and why does it matter in the context of BI and analytics in my organization? Implementing and supporting successful analytics depends on far more than the selection of a leading tool. At the end of the day, widespread, successful adoption of the system for actual value, trust in the data for decision making, and a belief in the underlying data and management processes are all crucial. Effective governance puts these in place. For more information, check out this whitepaper on the importance of Power BI governance.
- Self-Service BI: The goal of self-service BI is to empower users to create their own dashboards and reports using an organized and governed information architecture supported by IT. This lets IT leverage finite technical experts for topics that require technical skills (data integration, etc.) and moves the non-technical work (choosing a visualization, picking colors, etc.) into the hands of the users. This combines the benefits of speeding delivery of user requirements while focusing IT ROI where it drives the most benefit. To learn more about getting this process started, check out this post on how to plan a Power BI rollout.
- Parallel Processing for the Data Warehouse: Imagine applying the benefits of parallel processing to your analytics environment. Microsoft offers both Analytics Platform System (APS) for customers who would like to do this on-premises and the cloud equivalent called Azure SQL Data Warehouse. Our experience is that these can yield tremendous (50:1) performance gains, but they require solid implementation and key differences in SQL query logic to take advantage of the strengths of splitting work up among multiple worker nodes.
- Data Storytelling: What is data storytelling, and what kinds of scenarios make it valuable beyond mere collections of dashboards and reports? See this article from Jen Underwood, a longtime industry expert.
- Text analytics and sentiment analysis: How are text analytics and sentiment analysis practically applicable to my operation? What are some ways in which I can structure text data for analysis? See this blog article by David Eldersveld on how to bring structure to your data.
Ensure that you understand these concepts and utilize them for your organization’s success, and you won’t be left behind by your competitors. For more information, here’s a CIO magazine article that lays out other market trends in analytics.
If you have an interest in learning more about these topics, check out BlueGranite’s offers! Additionally, if you have any questions or comments, feel free to drop us a line and we will be happy to help however we can.