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Where Predictive Analytics Is Being Used - Where within the organization do we find predictive analytics being used? Predictive analytics historically has been in two areas. One of the most common uses has been within the marketing organization looking at the likelihood of individuals’ potential purchasing behavior of products and services. What are the influences and impressions of advertising? We’ve also seen predictive analytics used quite a bit for determining customer behavior. How do we optimize our interactions with our customers knowing that they’re going to react in certain ways based on their demographic profiles or their previous purchasing habits. We’re also seeing now how predictive analytics can be used across the manufacturing supply chain. We’re looking at things like mean time between failure and other particular processes that are well defined. Potential results will be known based on how things are operated. Besides marketing, customer facing issues and the supply chain, we also do see that many organizations are starting to apply predictive analytics into areas such as sales and finance and also looking at now the potential use of them in regards to working with suppliers and where materials are coming from to actually manufacturer products. Since there’s a lot of information that’s outside of our organization, we can start using predictive analytics as guideposts to everything from potentially prices of how companies stock is trading down to the commodity pricing of materials that are used for manufacturing products as well...
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Why are BI and data analytics important in the construction industry? -Without a clear-cut vision or definitive ways to measure or benchmark progress, many construction projects fall behind key deadlines while overshooting their budgets. The key reason why using analytical BI solutions and dashboard software in the construction industry is so important comes down to the sheer scale or scope of the task at hand. If you work with BI-boosting dashboard software, your construction company will...
Why Big Data Analytics Is So Important In Government - Today we’re going to talk about why big data analytics is so important in government globally, and especially in this economy. Commercial industry has been using data. The FedEx’s, the Wal-Mart’s, companies are really taking advantage of using their data, viewing it as an asset, making better decisions, faster decisions, and responding to shifts in the marketplace. Certainly in today’s environment the government is very interested in trying to do those same things. Budgets are shrinking. There are shortfalls in revenue. They just have to get smarter and better at what their doing. The Obama administration is out there promoting transparency, promoting visibility into what the government does, how they spend their money, the decisions they are making, and passing to on to every single citizen. So what they are doing today to put that in front, increase that visibility, make better decisions around policy, understand the direction of the country, itself, is very critical. In terms of where in the government the move towards better analytics is taking place, it’s in the financial departments of the various agencies. They are leading the way in terms of increasing that visibility of spending levels. Also, with healthcare reform, there is interest in knowing what is happening with costs and the aging population...
Why Do Companies Need Analytics? - Let’s start with a simple question of why? Why is it that companies need analytics? What is it that’s driving the urgency? The answer to that question is really closely tied to some of the main trends that we talked about before in terms of data users and time. So at the top of the list once we get once again we see data. Companies feel that all too often their most critical decisions are based on data that they have little faith in, either because it’s inaccurate, it’s dirty, it’s incomplete or sometimes just too fragmented or siloed within different departments. As we see with the third pressure on the chart here, the pressure also becomes evident from that business user perspective, that tactical or operational level. Companies struggle with getting the visibility they need into their key processes and then understanding what it all means. They look at analytics to help really sift through that operational data to produce insights that can help improve those processes. And lastly, we again see the urgency for analytics. A common reason why companies implement analytics, well it’s because people are clamoring for it. And not always technical people, line of business decision makers across many areas of the company again are raising their hands and asking for that better and deeper analytical capability...
Why Hotels Use Big Data Analytics to Improve Their Performance - Big data as a concept has earned tremendous popularity over the last few years, but most entrepreneurs still believe that it is strictly reserved for international corporations and high-end IT projects. The truth is exactly the opposite since data analytics is already capable of helping businesses of all sizes thrive and grow long-term. The hospitality industry is not an exception to this rule as top-performing hotels already utilize the power of big data to boost performance and maximize the profit. You are probably wondering: How is that possible...
With Predictive Analytics It's Individualized Decision Making - In that particular case while the customer's claim is not being fast tracked, they are happy because they have an understanding of the length of time it will take to process their claims so they're not operating in the dark. Without predictive analytics, essentially it's simple rules based decision making, but it's more of a one size fits all manner of application.
Whereas, with predictive analytics it's individualized decision making, and it's tailored to the behavior of the customer and other related data. The main benefit I guess in this particular instance of applied predictive analytics was the time to resolution. The claim is being resolved in a much shorter time, and the number of contacts required between the customer and the insurance provider has been reduced which leads to an increase in customer satisfaction and lower cost. I would also like to take a moment to remind you of the ability to use unstructured data to inform decision making. There was a lot of pretext information out there from interactions during phone calls that may be posted in social media. We have essentially seen and conducted analyses of the type of data, and we can see how it can be used to understand what is it these customers are actually saying.
Also applying an appropriate framework to the study of these type of data ensures that you have an objective and repeatable and automated analytics process. Don't forget that the unstructured data is very valuable also...