Big data: 4 predictions for 2014
One could look back at 2013 and consider it the breakthrough year for big data, not in terms of innovation but rather in awareness. The increasing interest in big data meant it received more mainstream attention than ever before. Indeed, the likes of Google, IBM, Facebook and Twitter all acquired companies in the big data space. Documents leaked by Edward Snowden also revealed that intelligence agencies have been collecting big data in the form of metadata and, amongst other things, information from social media profiles for a decade.
And beyond all of that, big data became everyone's most hated buzzword in 2013 after it was inappropriately used everywhere, from boardrooms to conferences. This has led to countless analysts, journalists and readers calling for people to stop talking about big data.
1. In 2014, people will finally start to understand the term big data. Because, as it stands, many do not.
2. Consumers will begin to (voluntarily) give up certain elements of privacy for personalization.
3. Big data-as-a-service will become a big deal
4. And finally... remember how Hadoop is an open-source software? Expect a lot more of that.
Article source: The Guardian
The value of Big Data: How analytics differentiates winners
Big Data is quickly becoming a critically important driver of business success across sectors, but many executives say they don’t think their companies are equipped to make the most of it. Bain & Company surveyed executives at more than 400 companies around the world, most with revenues of more than $1 billion. We asked them about their data and analytics capabilities and about their decision-making speed and effectiveness.
The results were surprising: We found that only 4% of companies are really good at analytics, an elite group that puts into play the right people, tools, data and intentional focus. These are the companies that are already using analytics insights to change the way they operate or to improve their products and services. And the difference is already visible.
These companies are:
1. Twice as likely to be in the top quartile of financial performance within their industries
2. Three times more likely to execute decisions as intended
3. Five times more likely to make decisions faster
In our analytics survey, 56% of the companies didn’t have the right systems to capture the data they needed or weren’t collecting useful data, and 66% lacked the right technology to store and access data. A good data policy identifies relevant data sources and builds a data view on the business in order to—and this is the critical part—differentiate your company’s analytics capabilities and perspective from competitors. A critical aspect of good data policy is to focus on identifying relevant sources of data.
In our survey, 56% of executives said their companies lacked the capabilities to develop deep, data-driven insights. Most agreed they were not up to the challenges of identifying and prioritizing what types of insights would be most relevant to the business. Successful analytics teams build those capabilities by blending data, technical and business talent.
Article source: Bain & Company
Separating Good Data Sources from Bad
The right list is often cited as the key to direct marketing success. But can you tell good data sources from bad? Whether you are looking for new customers, measuring market share or mining competitive intelligence, it all starts with the source.
When it comes to finding credible and reliable sources data seekers must proceed with caution. Think about it. You wouldn’t buy expensive operating software for your business from an unknown, unsubstantiated company. Why risk your firm’s hard-earned dollars on data from a source chosen in the blind? In today’s world data can quickly become out of date. People move and change jobs; companies pursue different new markets. Data sources that don’t stay on top of change will waste your marketing dollars. Call the data source company to learn more about them. Seek out their customers or suppliers. Ask the right questions.
Here are eight “due diligence” questions to ask while verifying a data source.
1. From where did the data originate?
2. Is the data generated from substantiated entities?
3. Does the source provide samples of data offered, such as snippets of reports, listings or the exact data you seek?
4. Is the source’s data timely and up-to-date? How often do they maintain the data: Monthly, weekly, or daily?
5. Can the source provide you with references from users of similar data? Are their customers satisfied?
6. Do they educate prospects about selecting their data?
7. What support and analytical tools does the source provide? Today, supplying data isn’t enough. The source should make it easy for you to utilize data to its fullest potential.
8. When separating good sources from bad, the best question to ask may be this: Does the source manage due diligence so you don't have to?
Article source: Mauricio Jurin, EDA