A data lake is a centralized repository designed to house big data in structured, semi-structured and unstructured form. I have been covering the data lake topic for several years and encourage you to check out an earlier perspective called Data Lakes: Safe Way to Swim in Big Data? for background. Our data lake research has uncovered some points to consider in your efforts, and I’d like to offer a deeper dive into our findings.
Every organization performing analytics with multiple employees needs to collaborate. They should be collaborating in the analytics process and in communicating the results of those analyses. As I continue my evaluation of analytics and data vendors, I have to admit some disappointment at the level of collaborative capabilities some analytics vendors provide. To be fair, the level of capabilities vary widely, but I expected collaborative capabilities to be more uniformly available as a standard feature in analytics technologies by now. I had anticipated that three-quarters of analytics vendors would include collaboration capabilities. More than half the vendors I have evaluated support some comments and discussion in their products, only a few have incorporated social recognition and wall posting as part of their collaborative capabilities. So, what impact does a lack of analytics collaboration have on organizations undergoing digital transformation?
Artificial intelligence (AI) and machine learning (ML) are all the rage right now. Our Machine Learning Dynamic Insights research shows that organizations are using these techniques to achieve a competitive advantage and improve both customer experiences and their bottom line. One type of analysis an organization can perform using AI and ML is predictive analytics. Organizations also need to plan their operations to predict the amount of cash they will need, inventory levels and staffing requirements. Unfortunately, while planning begins with predictions, organizations can’t plan with AI and ML. Let me explain what I mean.
I was recently asked to identify key modern data architecture trends. Data architectures have changed significantly to accommodate larger volumes of data as well as new types of data such as streaming and unstructured data. Here are some of the trends I see continuing to impact data architectures.
MicroStrategy recently held its annual user conference, which focused on the theme of the “Intelligent Enterprise.” HyperIntelligence, an innovative product for delivering analytics throughout organizations that they introduced a year ago, was the star of the event. The company announced enhancements to HyperIntelligence and the latest version of its flagship platform, MicroStrategy 2020, as well as a new two-tiered education and certification program.
Organizations’ use of data and information is evolving as the amount of data and the frequency with which that data is collected increase. Data now streams into organizations from myriad sources, among them social media feeds and internet-of-things devices. These seemingly ever-increasing volumes of devices and data streams offer both challenges and opportunities to capture information about a business and improve its operations.
The emerging internet of things (IoT) is an extension of digital connectivity to devices and sensors in homes, businesses, vehicles and potentially almost anywhere. This innovation means that virtually any appropriately designed device can generate and transmit data about its operations, which can facilitate monitoring and a range of automatic functions. To do this IoT requires a set of event-centered information and analytic processes that enable people to use that event information to make optimal decisions and take act effectively.
Organizations now must store, process and use data of significantly greater volume and variety than in the past. These factors plus the velocity of data today — the unrelentingly rapid rate at which it is generated, both in enterprise systems and on the internet — add to the challenge of getting the data into a form that can be used for business tasks.
About 10 years ago, social media tools like Facebook, Twitter and LinkedIn introduced a wave of collaborative analytics and BI capabilities. We saw chat streams associated with specific analyses that users could like or endorse. The number of contributions a user made to the community was part of his or her profile so others could accordingly weigh the importance of the input.
I am happy to offer some insights on Qlik drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated Qlik and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.