Three Reasons Why Your Users Should Care about IoT data

Three Reasons Why Your Users Should Care about IoT data

Data is data, right? It varies in quality and origin, but it all winds up in my dashboard. It couldn’t have a material impact on the way I make decisions…right? At a typical data-driven company, the decision process is fickle and fast-changing. Technology plays an important role in the way we transform data into insight, as does organizational maturity. Companies that have the right processes and internal capabilities in place are simply better positioned to exploit the potential of analytics and make better decisions. However, Aberdeen Group’s research suggests that the type of data used for analysis can also impact the decision process and boost user satisfaction. According to recent research exploring companies that use Internet of Things (IoT) data frequently, there are several factors of decision-making that strongly correlate with the usage of this type of information. 1. Timely information Research shows that IoT shops have a greater urgency for information, yet they’re more likely to get it on time. The volume and constant propagation of IoT data can certainly create challenges for companies, but it also creates opportunities to get real-time data into the hands of users faster. 2. Data quality To a substantial degree, issues with data quality are born out of human error. Erroneous entries, missing fields, multiple versions of the same data, etc. Due to its nature of being machine-generated, IoT data is less prone to these types of quality issues and Aberdeen’s survey respondents validate that claim. 3. Analytical firepower Any company using IoT data frequently is most likely engaged in analytical activities beyond just simple static reports. When handled properly, this type of...
Alteryx Inspire 2016: Is There a Cure for Data Hate?

Alteryx Inspire 2016: Is There a Cure for Data Hate?

Returning from Inspire 2016 –the annual user conference from Alteryx – as I write this, I can’t get away from one burning question rattling around my brain: Why do people hate their data so much? Before delving into that one, let’s provide a little background here. Alteryx likes to distinguish itself as a “platform” –as opposed to a point solution – for self-service data analytics. Including capabilities for data blending (some have called it integration), data preparation, enrichment, and predictive/advanced analytics, the solution is geared primarily toward data scientists and business analysts, especially those that deftly balance a modicum of technical acumen with a healthy dose of business expertise. Accordingly, the Inspire conference is chock full of folks whose primary job function involves extracting meaningful insight from data, a task monumentally easier said than done. Through casual conversations, and after listening to several “before and after” style customer accounts, a common thread that emerged was a deep dissatisfaction with the raw material used to create insight. So, why do people hate their data? There are several reasons (in no particular order): It’s gross. Riddled with corrupted, duplicated, incorrect, or absent fields, the typical data source has major quality issues. To make matters worse, these issues arise regardless of data source, be it an application, an operational data store, a data warehouse, or just a spreadsheet. You need the Keymaster of Gozer just to find it. In the heat of analysis, with all the brain-burning and number-crunching, how often does someone need data that isn’t immediately at their fingertips? All. The. Bleeping. Time. For many of these poor souls, adding a field to a data set, or, God forbid,...
Three Key Criteria When Selecting Data Visualization Tools

Three Key Criteria When Selecting Data Visualization Tools

So how many rows of data did you say this tool can ingest? It’s backed by a massively parallel processing engine? It runs in-memory? And…why should I care about any of this? If flexibility, firepower, and adaptability were the only considerations for technology implementation, these decisions would be easier. The fickle business user community, though, needs more convincing than raw numbers on paper. When it comes to tools like interactive visualization, for those that survive or thrive based on business user adoption, connecting with a broader community is absolutely vital. Recent Aberdeen research explored the impact of data visualization and found that, in addition to greater analytical engagement, these users shared three common characteristics of satisfaction. 1: Ease-of-use Interactive visualization is all about exploring the data behind the data — the “why?” behind what is presented. All too often though, the tools don’t resonate with the business users typically asking those questions. Ease-of-use can be difficult to describe, and varies from company to company, but the general concepts apply broadly. Users need intuitive, drag-and-drop, easy drill-down capabilities in order to explore the data. 2: Data connectivity Nothing kills the momentum of analytical activity like hitting the invisible wall of data absence. In the heat of an analysis, users need the ability to pull in data from sources not necessarily presented to them in the original dashboard or visualization. The ability to connect to, and ingest information from, other sources is a key enabling factor of interactive visualization. 3: Line-of-business fit Closely tied to ease-of-use, users need tools that fit the logic and taxonomy of their business area. Sometimes that involves...
Can Your Business Intelligence & Analytics Truly Adapt? [Webinar]

Can Your Business Intelligence & Analytics Truly Adapt? [Webinar]

We’ve talked before about how managing Big Data in the enterprise is a double-edged sword — there’s a lot of it coming into the business at a breakneck pace, but moreover, it’s more complex than ever. Add to this the fact that many organizations lack real-time insight into customer needs and behavior, and that data-savvy employees of these companies can’t easily access data and analytical insight when they need it most. This isn’t a surprise, really — traditional on-premise business intelligence (BI) platforms, although effective at creating static views of historical information, fall far short when it comes to flexibility and speed. So the questions really become for companies: How can I transform Big Data into “Fast Data” and hasten the decision process, so I can better serve my customers? Is my BI platform adaptable enough right now to even make this a reality? To expand upon the solution to this customer service problem plaguing many enterprises, Aberdeen Group VP and Principal Analyst Michael Lock will discuss findings from his latest research in a live webinar on Thursday, June 9th, at 1 PM EDT. In the session entitled “Building an Adaptive Approach to Business Intelligence and Analytics,” you’ll learn: What business pressures drive the need for new approaches to BI Key practices that empower the analytical workforce Top strategies for understanding and meeting the needs of today’s customers Be sure to register for the live webinar at 1 PM EDT on Thursday, June 9th. Even if you can’t make this time, be sure to register anyways — we’ll send you a link to the on-demand recording...
Stat of the Week: Fast Data Through Cloud Analytics

Stat of the Week: Fast Data Through Cloud Analytics

Any business knows the drill — there’s more data than ever before, complex as ever, coming in faster than ever before. But cloud-based analytics in particular has become a tool to empower a wide variety of business users with faster access to relevant data, as this Stat of the Week shows. Be sure to also check out the related content brief, Unleashing the Power of Analytics in the...
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