How Utilities Are Deploying Data Analytics Now

How Utilities Are Deploying Data Analytics NowUtilities are sitting on a wealth of opportunity from data analytics, with more information than ever before flowing from smart meters and other sensors, along with traditional sources of data about their operations. Most utility executives see the potential to mine this data for insights, even if they aren’t quite sure how or where to start. They know these discoveries will eventually generate tremendous value for their organizations, but they tend to think this will be years away, after enormous investments to enhance systems and improve the quality of data.

The good news is, they don’t need to wait. Utilities already have access to data and tools that they can use to begin deploying production analytics and generating insights that create value. They are improving their ability to analyze data and understand their business. For example, some have already sharpened their accuracy in predicting equipment failures and power-outage durations—results that can reduce costs and increase customer satisfaction.

This article originally appeared in bain.com .  To read the full article, click here.

How Analytics Is Aiding Banking Compliance

How Analytics Is Aiding Banking ComplianceRegulations are costly and time consuming for banks, and they need to stay on top of their data.

Banks struggled to put this information together, highlighting a complacency and malaise that likely exacerbated the problems of the crisis. Lehman Brothers’ collapse heralded the beginning of a new era of regulations, though, with Dodd Frank, which was introduced in 2010, and Basel III in 2011 among the most far-reaching and complex. Over $100 billion in fines have been paid in US for non-compliance since 2007, and with a new Republican-led regime entering power, it is unclear what the future holds.

The time and cost of regulatory compliance and reporting vastly increases with every new regulation. Regulatory bylaws must, by their very nature, be thorough, and many contain hundreds of pages of information. Keeping up with these causes additional stress to financial services institutions, at a time when new competition from FinTech is creeping up the sides.

This article originally appeared in Innovation Enterprise .  To read the full article, click here.

How CEOs Can Keep Their Analytics Programs from Being a Waste of Time

How CEOs Can Keep Their Analytics Programs from Being a Waste of TimeDespite billions of dollars invested in big data and analytics, the simple truth is that most projects and programs fail to meet expectations. And we have figured out why: analytics forces changes on the C-suite that the CEO has to anticipate and manage, but many don’t.

From how we choose presidents to what movies we choose to watch, big data and analytics have become integral parts of our lives. But for too many companies, analytics is an unsolved puzzle with the pieces flung all over the floor.

This article originally appeared in hbr.com .  To read the full article, click here.

Four Roadblocks to Becoming Data-Driven, and How to Overcome Them

Four Roadblocks to Becoming Data-Driven, and How to Overcome ThemToday’s most competitive companies are data-driven. Consider Uber, which assailed the taxicab industry in San Francisco in just a few years, without owning a single taxicab. How? Among many innovations, Uber brought data to the taxi industry. Using historical data, Uber advises drivers to be in certain hotspots during certain times of day to maximize their revenue because customers tell them with the push of a button where to be.

These companies don’t rely on hunches, siloed spreadsheets, or data on rogue servers to make decisions; instead, they have operationalized data as a part of every process and decision and built cultures where guesswork doesn’t suffice. Operationalizing data, or using data to improve business performance, will be the defining competitive advantage of the future.

 

This article originally appeared in DataInformed .  To read the full article, click here.

The Business Importance of Fast Data, Real-time Analytics & Transaction Tracking

fast dataEnterprise-Grade Digital Transformation

Lately, there’s a lot of talk about something called “digital transformation”. Is this really,”a thing”?  Apparently, it is.  Digital transformation is the process of shifting your organization from a legacy approach to new ways of working using emerging technologies. Continue reading

Using Big Data Analytics to Improve IT Operations

Using Big Data Analytics to Improve IT OperationsTo deliver high-performance and stable IT operations, staffs end up taking shortcuts, bypassing automated and manual processes. Eventually, it leads to applicaton defects and infrastructure failures. Nevertheless, IT departments need to be able to control their environments, diagnose and pre-empt problems and incidents. Unfortunately, most of existing IT tools don’t collect adequate data and don’t do a robust job of handling the data they collect. Activity dashboards are cluttered, and have too many alerts that users end up ignoring them.

Making Big Data Analytics Actionable for IT

An organization’s IT environment typically generates Terabytes of data about system metrics, change logs, event logs and other operational data. It is possible to obtain extremely granular data about the history and current state of your IT environment. The key is to make this data actionable. However, the challenge is to do it rapidly, with minimum overhead. As IT is required to drive more output with fewer resources, IT specialists don’t have months to invest it training, rollout and deployment of a typical enterprise solution. The same specialists need to deal with day-to-day IT issues. This leaves them with even lesser time to proactively manage operations.

Data Analytics for IT Operations

The rise of IT Operations Analytics (ITOA) makes IT’s big data usable by blending and correlating this data, automatically coming up with actionable insights to manage & improve operations. This process means collecting as much data as possible, to avoid missing any critical information, and narrowing it down to useful conclusions. The business side of organizations have already adopted this approach, running operations in real-time. Ironically, although IT is supplying the business users with the necessary big data analytics they need, IT itself is lagging in this respect.

In the past, the business side of organization faced a similar challenge due to the growing pile of structured and unstructured data. Currently, business users deal with Big data using technologies for managing and analyzing large, diverse sets of data. These Business Analytics tools are capable of processing large amounts of data in various formats from anywhere, and correlate data to provide business managers & executives with new insights to help run their business. They can be used to design & develop systems for ITOA.

ITOA can provide meaningful insights based on domain understanding of operational data. This will provide IT operations teams visibility into the behavior of business sytems, enable them to automatically identify and isolate critical events, such as system changes, that have the potential to disrupt existing sytems. ITOA enables you to quickly identify potential issues upfront and understand these issues from the mountain of raw data collected by various monitoring systems. This empowers IT operations to efficiently determine the best way to restore systems, meet performance and availability expectations.

Change-driven Data Analytics

IT must adopt a change-driven approach to deal with operational issues swiftly. Changes are a major source of IT issues. Every time there are changes in application, data or infrastructure, business systems are exposed to risks. By focusing on changes and their impact, IT specialists can analyze data about performance, availability, security; identify actual causes and potential issues.

With the help ITOA, IT operations teams can constantly monitor how changes are affecting various systems, what risks they introduce, use data to gain actionable intelligence and respond quickly.

 

 

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MIT’s answer to global health issues: Democratizing big data analytics

MIT's answer to global health issues: Democratizing big data analyticsDiscover how medical professionals and MIT researchers are using data-enabled systems to help doctors around the world make the best healthcare decisions.

If you think it’s hard to keep up with all the new software and hardware innovations, imagine doctors trying to stay abreast of medical advances.

“While wonderful new medical discoveries and innovations are in the news every day, doctors struggle with using information and techniques available right now,” writes Leo Anthony Celi, assistant professor of medicine, Harvard Medical School, in the Conversation commentary Improving patient care by bridging the divide between doctors and data scientists. “As a practicing doctor, I deal with uncertainties and unanswered clinical questions all the time.”

Enter big data

Celi feels there are opportunities for big data and information analytics in the healthcare field. “A digital system would collect and store as much clinical data as possible from as many patients as possible,” writes Celi. “It could then use information from the past—such as blood pressure, blood sugar levels, heart rate, and other measurements of patients’ body functions—to guide future doctors to the best diagnosis and treatment of similar patients.”

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5 Ways Banks Use Big Data Analytics To Win Back Customer Confidence

5 Ways Banks Use Big Data Analytics To Win Back Customer ConfidenceDespite the industry’s robust growth and the fact that banks have been an integral part of the fabric of society for hundreds of years, the public view the banking community with suspicion. Customers focus on issues like security breaches, lack of service expansion, and poor customer service, while the banking fraternity look to the heavens and downplay their concerns.

Winning hearts and minds

To win back customer confidence and maintain their place in the face of revolutionary digital disruption, individual banks (as well as the industry as a whole) need to take a long hard look at their traditional business models and operational practices. Some banks have already begun the digital transformation journey – adopting new technologies and tapping existing data resources to develop better products and services. Big Data and Analytics are the key but largely, their full potential still remains unrealised. Banks need to take some practical steps towards turning consumer-perception obstacles into data-driven business opportunities.

Payments data

Start with the most under-appreciated dataset. Payments reveal a great deal about each user – how much they’ve paid, what they paid for, who was paid, the banks involved, transaction time and location, and so on. In fact, a customer’s payment profile says much more about her, or him, than any social media metric or record. Payments data is highly accessible and can pinpoint lifestyles, detect which companies make up a supply chain, and plot spending trends by time or place. At the same time, although customer data is not as dynamic as payments data, in banking systems it can be attached to other profiles such as payments and credit history to enhance analytics and create successful “Next-Best-Offers”.

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How HR Departments Can Obtain and Use Big Data

How HR Departments Can Obtain and Use Big DataEnterprises are constantly seeking new ways to use analytics across their organizations, and the human resources department is no exception. According to a Towers Watson survey, companies are spending a good portion of their HR technology budgets on big data and analytics to improve the hiring process, retain employees, and make better business decisions. While big data can give HR a seat at the overall decision-making table when used correctly, many companies struggle with obtaining the data. Additionally, those in possession of hiring and employee analytics struggle with how to best use this data in a way that matters.

The following is an exploration of how HR departments can leverage big data analytics to become strategic business partners in their organizations.

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2016 Big Data, Advanced Analytics & Cloud Developer Update: 5.4M Developers Now Building Cloud Apps

2016 Big Data, Advanced Analytics & Cloud Developer Update: 5.4M Developers Now Building Cloud AppsKey takeaways from the study include the following:

  • 6M developers (29% of all developers globally) are involved in a Big Data and Advanced Analytics project today. An additional 25% of developers, or 5.3M, are going to begin Big Data and Advanced Analytics projects within the next six 13% or 2.6M of all developers globally are going to start Big Data and Advanced Analytics projects within the next 7 to 12 months.  The following graphic provides an overview of the involvement of 21M developers in Big Data and Advanced Analytics projects today.
  • 4M developers (26% of all developers globally) are using the cloud as a development environment today. Developers creating new apps in the cloud had increased 375% since Evans began measuring developer participation in mobile development in 2009 when just slightly more than 1.2M developers were using the cloud as their development platform. 4.5M developers (21% of all global developers) plan on beginning app development on cloud platforms in the next six months, and 3.9M (18% of all global developers) plan on starting development on the cloud in 7 – 12 months.

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