Data capture techniques fail to keep pace with demands for real-time analysis
Organizations using extract, transfer and load (ETL) and Changed Data Capture (CDC) technologies are struggling to keep up with today's demand for real-time data analysis, with negative effects on their business opportunities and efficiency.
This is one of the findings of a new study by IDC, sponsored by software company InterSystems which highlights a growing need for reliable real-time data analytics in today's enterprise.
Three ways to generate profit with the data you already have
Build it and they will come. That is the view many organizations maintain about their data lakes and data warehouses. Companies are rapidly investing in systems and processes to retain business data that they know is valuable but have no clue what to do with it. Even the government collects mass amounts of data without specific plans for using the information at the time of collection. This trend only accelerates as the amount of data being produced continues to escalate. Today, it is estimated that human knowledge is doubling every 12 to 13 months and IBM is estimating that with the build out of the "internet of things," knowledge will double every 12 hours.
Most organizations search for value in their data by throwing teams of data scientists at the various stores of data collected hoping to find insights that are commercially viable. This approach typically results in endless hours of digging for insights and if any are found, they rarely see the light of day. In order to monetize your data, you need a different approach, one that starts by turning the process on its head. We recommend three approaches to help you monetize your data:
Have a business challenge? It's really a big data problem in disguise
Throughout history, businesses have relied on certain measures to project their success -- sales growth, customer retention, churn rate, and so on. However, those are all lagging indicators for modern businesses that want to stay ahead of the competition; all these potential business challenges must fall under a single new metric: time to insight. Similarly, think about what happened with high-frequency trading, where suddenly, years of research and algorithms didn’t matter if your competition could act on information orders of magnitude faster than you -- it was like having a crystal ball to the future!
The advent of big data has companies flooded by a tsunami of information. The problem with this is twofold: big data is difficult to wrangle in order to provide insights in the first place, and once the right architecture is deployed, it can then be hard to choose the proper parameters to provide those insights.
Sharing of patient data between Royal Free hospital and Google DeepMind breached Data Protection Act
A partnership between London's Royal Free hospital and DeepMind resulted in a breach of the Data Protection Act, an investigation by the Information Commissioner's Office (ICO) has concluded.
The personal data of more than 1.6 million patients was transferred to the Google subsidiary as part of the creation of Streams, an app to diagnose and detect acute kidney injury. The ICO found that patients were not properly informed about how their data would be used, and highlighted a "number of shortcomings" in the way data was handled.
New platform helps integrate big data across the cloud
As enterprises continue to move their data to the cloud, they often find themselves using multiple different services. But a multi-cloud approach brings problems when it comes to managing and integrating information.
To tackle this issue, cloud and big data integration company Talend is releasing a new version of its Talend Data Fabric integration platform that is optimized to manage multi-cloud, enterprise IT environments.
New digital assistant answers data-related questions
In our private lives we're used to getting answers quickly from digital assistants. So far though this is something that hasn't really taken off in business environments.
That could be about to change as data search specialist Knoema launches Yodatai, a bot that's designed to answer data-related questions.
Trump's 2016 Big Data political arms race
Events happen so quickly in the wacky whirlwind world of Donald Trump that it’s hard to react in anything close to real time, but there was an interesting story in the Guardian last weekend that I think deserves some technical context.
The Great British Brexit Robbery: How our Democracy was Hijacked is a breathless but well sourced story about how a U.S. billionaire harnessed Big Data to split up the European Union and steal a U.S. Presidential election. It’s an interesting read, but the point I want to make here is that the tale was entirely predictable and if one side hadn’t done it, the other would have. Next time they’ll all do it.
Hire and retain talent to make it in the digital economy
The way we work is transforming at an extraordinary rate. Explosive progress in the technology industry, demonstrated by the fact that digital tech investment reached £6.8 billion last year, creating 1.64 million jobs as a result, is unavoidable. Despite this positive influx of job roles, there is a mismatch between the volume of positions available and the skills that are required to do them.
To be able to maintain a grasp of its competitive edge within a global marketplace, the UK must begin to tackle the ever-increasing skills gap.
The future of self-driving cars relies on big data
Autonomous cars depend on information. They’re equipped with technology to gather and communicate a vehicle’s position, speed, direction, and braking status. They also recognize traffic signals, proximity to pedestrians, and hazardous objects on the road. With voluminous data being computed in near real-time, self-driving cars can respond to dangers afoot or notify the car owner of imminent mechanical issues, in which case it might automatically set up an appointment with the nearest auto body shop.
In addition, self-driving vehicles aren’t disposed to human-prone errors resulting from distracted driving, tired driving, or driving while inebriated. They’re constantly alert, vigilant, and have a panoramic view of the road. All of this demonstrates how big data is becoming the cornerstone to the driverless car’s future. What’s more, it’s already making great strides toward self-driving capabilities, driver safety, and enhanced customer experience.
Big data will be a $76bn market by 2020
Big Data is now a $57 billion market, according to a new report from SNS Research. Entitled The Big Data Market: 2017 - 2030 -- Opportunities, Challenges, Strategies, Industry Verticals & Forecasts, it says that vendors will be earning from hardware, software, and professional services revenue.
What’s more, the report says the investments are expected to grow at a CAGR of roughly 10 percent over the next four years, reaching $76 billion by the end of 2020.
New integration delivers improved security analytics for mainframe users
Many enterprises still rely on logs and data from a range of different security products to get a picture of user behavior, particularly where legacy mainframe systems are involved.
A collaboration between data analytics company Syncsort and application audit specialist Compuware is aimed at improving an organization's ability to detect threats against critical mainframe data, correlate them with related information and events and satisfy compliance requirements.
Put the BI in Big Data by going data native
Customer drop-off, supply chain problems, lack of competitor awareness -- there are a plethora of critical business problems out there, but the answer is a panacea -- actionable big data. For any company struggling to understand what direction to take its business decisions, there is often a disconnect between its business intelligence (BI) and its big data.
Companies are amassing more data than ever, with Gartner predicting it will continue to grow by 800 percent over the next five years -- yet 80 percent will be unstructured. Herein lies the breakdown -- companies need robust storage, processing, and analysis to get that unstructured data to be actionable and, therefore, capable of stopping critical business problems in their tracks. But many companies’ existing data architecture investments are holding them back. They are hamstrung by their legacy business intelligence and data visualization tools that aren’t geared toward solving complex big data problems.
New cloud platform enables cost-effective data engineering
Businesses encounter a variety of challenges in building systems on and around Spark to meet the needs of data engineering.
Often engineers need to perform mission-critical data cleansing, transformations, and manipulations, to make business activities real-time dashboards or fraud detection possible. Mastering data engineering is therefore an essential step to automating systems and making data-driven decisions.
New platform delivers crowd insights into consumer preferences
A combination of big data and the web means that there's more opportunity than ever for companies to study and understand the habits of consumers.
Online lists and voting site Ranker is using its wealth of polling data to launch a new Ranker Insights platform for marketers to uncover details of consumer behavior.
Intelligent MDM puts data in the hands of business users
Enterprise information management (EIM) and master data management (MDM) have in the past been complex tasks that have meant tight control of data by the IT department.
Now though MDM specialist Semarchy is launching a new intelligent solution called xDM, designed to deliver control of data to the business user.
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