​How to optimize your big data strategy

How to optimize your big data strategy

A blog around the latest happenings in big data, including tutorials and tips on what you can do to stay ahead of the game.

It’s clear that big data is a game-changer–some call it the new oil, while others are convinced it will be the fuel of the fourth industrial revolution. But how do you actually get started with your big data strategy? How do you build a big data infrastructure? And how do you make sure that your big data use case actually delivers value for your organisation? Our experts share their insights on how to optimize your big data strategy.

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Big data has officially taken the tech world by storm, and the mainstream IT community is all but caught up in the excitement. When it’s not discussing the latest big data acquisitions, partnerships and developments, the tech community is busy delving into new ways to optimize big data analytics, looking for new methods to derive value from the massive amounts of information we create on a daily basis.

As with other fast-moving tech trends, there’s no shortage of big data news to be found online. But as big data becomes more widespread, it’s also becoming increasingly complex. One can only imagine how daunting this constant stream of information must be for those who are just starting out with big data and are still working on getting their proverbial feet wet.

That’s why we’ve created this blog. Featuring articles on everything from tips for improving your big data analytics strategy to interviews with leading experts in the field, this blog will serve as a one-stop shop for anyone just beginning their journey into the wide world of big data.

Big Data is a hot topic in the IT industry. So much so that it’s hard to know where to start, when you’re trying to optimize your Big Data strategy. Here are three things to think about before you begin.

Big Data Is a Big Deal

Big data is a big deal for your business because it can help you make better decisions through analysis of large sets of data. The more information you have on your customers or products, the better decisions you can make about what they want, as well as how to market and sell them.

But big data isn’t just about analyzing lots of data; it’s also about getting that data stored efficiently so it doesn’t take up too much space. That’s why companies like Amazon and Google are using distributed storage systems that keep their data on multiple servers around the world instead of one central location.

This means they can access the information anywhere there is an Internet connection without having to worry about bandwidth issues or latency delays between servers. You should do the same thing with your own data if possible; having multiple copies ensures that nothing gets lost in transit or during processing time at any given point along its journey from creation until usage by consumers.

Consider Your Cloud Strategy

Because big data requires a lot of computing power,

Big data is a term used to describe the exponential growth and availability of data, both structured and unstructured. But it’s more than just a matter of size; it’s also about the speed at which it’s created and collected. Big data is often characterized by high volume, high velocity or high variety. One common thread that ties big data together is that modern businesses collect it at an accelerated rate to reveal insights and trends that were previously hidden.

The first step in any big data strategy is to understand your business requirements and objectives. These will differ from company to company, but generally speaking, there are four main directions you can take:

1. Operational monitoring

2. Customer intelligence

3. Product optimization

4. Risk management

Before evaluating solutions for your big data strategy, it’s important to understand how much you’re willing to spend on hardware, software, labor and skills development for yourself and your team (if you have one). Analytical tools can cost anywhere from free (open source) to $25,000 per user per year (closed source). Databases can cost anywhere from free (MySQL) to tens of thousands of dollars per terabyte — with no upper limit on the number of terabytes needed.

Information Technology is a complex business. It’s hard to know what’s coming next and even harder to make sure you’re prepared for it. Cloud computing, big data analytics, social media, mobile technology and the IoT are just some of the trends that have business leaders concerned about how they can adapt to a rapidly changing landscape.

To help you stay ahead of the curve, we’ve developed this guide to the most important technology trends that business leaders need to understand in 2016. From cloud computing to big data analytics, what you need to know is all here. This guide examines each trend, explains why it matters and offers guidance on how you can prepare your company for what’s next.

The Value of Your Personal Data, and How to Take Control of It

by Jennette Kilmer

March 31, 2017

Imagine you’re at the grocery store and you pick up some fruit. As you reach the register, the cashier scans the items and tells you the total price. But before you pay, the cashier says, “We have a special offer today! For just $1 more, you can have all these items for free next week.” You’d probably take her up on that offer! In fact, you might even be willing to pay $2 or $3 more for that same deal. When it comes to a good bargain, most people know how to recognize one when they see it. Especially if it involves food. But what about your personal data? How much is that worth? And how much should you be willing to pay for it?

When it comes to selling data about their customers’ behaviors and preferences, many companies are figuring out how to turn a profit from this type of information. In fact, a recent study by IDC Research shows that spending on big data and business analytics will grow from $130 billion in 2016 to more than $203 billion by 2020. And as we become more accustomed to sharing our personal data with companies

Analytics is not a new concept. It has evolved over the years, with new techniques and technologies enabling businesses to manage, analyze and derive value from their data. From simple descriptive analytics reporting on past results to predictive analytics that forecast future outcomes and prescriptive analytics that automate decision making, these analytical methods have been used by businesses for many years.

However, the term “big data” has only been around since 2008. What does big data mean? And what does it mean for big data analytics?

Big data refers to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. But the phrase is often used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. The value in big data comes from analyzing a large volume of information – both structured and unstructured – to reveal hidden patterns, unknown correlations and other useful business insights that can give an organization a competitive advantage.

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