What Is the Future of Data Warehousing?

Data Warehousing

There is no denying it – we live in The Age of the Customer. Consumers all over the world are now digitally empowered, and they have the means to decide which businesses will succeed and grow, and which ones will fail. As a result, most savvy businesses now understand that they must be customer-obsessed to succeed. They must have up-to-the-second data and analytical information so that they can give their customers what they want and provide the very best customer satisfaction possible.

This understanding has given rise to the concept of business intelligence (BI), the use of data mining, big data, and data analytics to analyze raw data and create faster, more effective business solutions. However, while the concept of BI is not necessarily new, traditional BI tactics are no longer enough to keep up and ensure success in the future. Today, traditional BI must be combined with agile BI (the use of agile software development to accelerate traditional BI for faster results and more adaptability) and big data to deliver the fastest and most useful insights so that businesses may convert, serve, and retain more customers.

Essentially, for a business to survive, BI must continuously evolve and adapt to improve agility and keep up with data trends in this new customer-driven age of enterprise. This new model for BI is also driving the future of data warehousing, as we will see moving forward.

Older BI Deployments Cannot Keep Pace for Success

As valuable as older BI applications and deployments have been over the years, they simply cannot keep pace with customer demands today. In fact, decision-makers in IT and business have reported a number of challenges when they have only deployed traditional BI. These include:

  • Inability to accurately quantify their BI investments’ ROI. Newer BI deployments implement methodologies for measuring ROI and determining the value of BI efforts.
  • A breakdown in communication and alignment between IT and business teams.
  • Inability to properly manage operational risk, resolve latency challenges, and/or handle scalability. While BI is intended to improve all of these, traditional BI is falling behind.
  • Difficulty with platform migration and/or integration.

Poor data quality. Even if data mining is fast and expansive, if the quality of the data is not up to par, it will not be useful in creating actionable intelligence for important business decisions.

Keeping Up with Customer Demand Through New BI Deployments

So how can combining traditional BI, agile BI, and big data help businesses grow and succeed in today’s market? Consider that big data gives businesses a more complete view of the customer by tapping into multiple data sources. At the same time, agile BI addresses the need for faster and more adaptable intelligence. Combine the two, along with already existing traditional BI, and efforts that were once separate can work together to create a stronger system of insight and analytics.

Through this new BI strategy, businesses can consistently harness insights and create actionable data in less time. Using the same technology, processes, and people, it allows businesses to manage growth and complexity, react faster to customer needs, and improve collaboration and top-line benefits – all at the same time.

The Drive for a New Kind of Data Warehousing

A new kind of data warehousing is essential to this new BI deployment, as much of the inefficiency in older BI deployments lies in the time and energy wasted in data movement and duplication. A few factors are driving the development and future of data warehousing, including:

  • Agility – To succeed today, businesses must use collaboration more than ever. Instead of having separate departments, teams, and implementations for things like data mining and analysis, IT, BI, business, etc., the new model involves cross-functional teams that engage in adaptive planning for continuous evolution and improvement. This kind of model cannot function with old forms of data warehousing, with just a single server (or set of servers) where data is stored and retrieved.
  • The Cloud – More and more, people and businesses are storing data on the cloud. Cloud-based computing offers the ability to access more data from different sources without the need for massive amounts of data movement and duplication. Thus, the cloud is a major factor in the future of data warehousing.
  • The Next Generation of Data – We are already seeing significant changes in data storage, data mining, and all things relate to big data, thanks to the Internet of Things. The next generation of data will (and already does) include even more evolution, including real-time data and streaming data.

How New Data Warehousing Solves Problems for Businesses

So how do new data warehouses change the face of BI and big data? These new data warehousing solutions offer businesses a more powerful and simpler means to achieve streaming, real-time data by connecting live data with previously stored historical data.

Before, business intelligence was an entirely different section of a company than the business section, and data analytics took place in an isolated bubble. Analysis was also restricted to only looking at and analyzing historical data – data from the past. Today, if businesses only look at historical data, they will be behind the curve before they even begin. Some of the solutions to this, which new data warehousing techniques and software provide, include:

  • Data lakes – Instead of storing data in hierarchical files and folders, as traditional data warehouses do, data lakes have a flat architecture that allows raw data to be stored in its natural form until it is needed.
  • Data fragmented across organizations – New data warehousing allows for faster data collection and analysis across organizations and departments. This is in keeping with the agility model and promotes more collaboration and faster results.
  • IoT streaming data – Again, the Internet of Things, is a major game changer, as customers, businesses, departments, etc. share and store data across multiple devices.

To Thrive in the Age of the Customers – Businesses Must Merge Previously Separate Efforts

Now that we are seeing real-time and streaming data, it is more important than ever before to create cohesive strategies for business insights. This means merging formerly separate efforts like traditional BI, agile BI, and big data.

Business agility is more important than ever before to convert and retain customers. To do this, BI must always be evolving, improving, and adapting, and this requires more collaboration and new data warehousing solutions. Through this evolution of strategies and technology, businesses can hope to grow and improve in The Age of the Customer.

Examples of the Future of Data Warehousing

And what exactly will the future of data warehousing look like? Companies like SAP are working on that right now. With the launch of the BW/4HANA data warehousing solution running on premise and Amazon Web Services (AWS) and others like it, we can see how businesses can combine historical and streaming data for better implementation and deployment of new BI strategies. This system and others like it work with Spark and Hadoop, as well as other programming frameworks to bring data and systems of insight into the 21st century and beyond.

Want to learn more about BI, agile BI, the future of data warehousing, and all things big data? 

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Source: What Is the Future of Data Warehousing? | Ronald van Loon | Pulse | LinkedIn

Machine Learning Becomes Mainstream: How to Increase Your Competitive Advantage

First there was big data – extremely large data sets that made it possible to use data analytics to reveal patterns and trends, allowing businesses to improve customer relations and production efficiency. Then came fast data analytics – the application of big data analytics in real-time to help solve issues with customer relations, security, and other challenges before they became problems. Now, with machine learning, the concepts of big data and fast data analytics can be used in combination with artificial intelligence (AI) to avoid these problems and challenges in the first place.

So what is machine learning, and how can it help your business? Machine learning is a subset of AI that lets computers “learn” without explicitly being programmed. Through machine learning, computers can develop the ability to learn through experience and search through data sets to detect patterns and trends. Instead of extracting that information for human comprehension and application, it will use it to adjust its own program actions.

What does that mean for your business? Machine learning can be used across industries, including but not limited to healthcare, automotive, financial services, cloud service providers, and more. With machine learning, professionals and businesses in these industries can get improved performance in a number of areas, including:

  • Image classification and detection
  • Fraud detection
  • Facial detection/recognition
  • Image recognition/tagging
  • Big data pattern detection
  • Network intrusion detection
  • Targeted ads
  • Gaming
  • Check processing
  • Computer server monitoring

In their raw data, large and small data sets hide numerous patterns and insights. Machine learning gives businesses, organizations, and institutions the ability to discover trends and patterns faster than ever before. Practical applications include:

  • Genome mapping
  • Enhanced automobile safety
  • Oil reserves exploration

Intel has worked relentlessly to develop libraries and reference architectures that not only enable machine learning but allow it to truly take flight and give businesses and organizations the competitive edge they need to succeed.
In fact, according to a recent study by Bain [1], companies that use machine learning and analytics are:

  • Twice as likely to make data-driven decisions.
  • Five times as likely to make decisions faster than competitors.
  • Three times as likely to have faster execution on those decisions.
  • Twice as likely to have top-quartile financial results.

Machine learning is giving businesses competitive advantages.

In other words, predictive data analytics and machine learning are becoming necessities for businesses that wish to succeed in today’s market. The right machine learning strategy can put your business ahead of the competition, reduce your TCO, and give you the edge your business needs to succeed.

Background on Predictive Analytics and Machine Learning

You already know that machine learning is essentially a form of data analytics, but where did it come from and how has it evolved to become what it is today? In the past couple of decades, we have seen a rapid expansion and evolution of information technology. In 1995, data storage cost around $1000/GB; by 2014 that cost had plummeted to $0.03/GB [2]. With access to larger and larger data sets, data scientists have made major advances in neural networks, which have led to better accuracy in modeling and analytics.

As we mentioned earlier, the combination of data and analytics opens up unique opportunities for businesses. Now that machine learning is entering the mainstream, the next step along the path is predictive analytics, which goes above and beyond previous analytics capabilities.

The Path to Predictive Analytics

With predictive analytics, companies can see more than just “what happened” or “what will happen in the future.”

Machine learning is a part of predictive analytics, and it is made up of deep learning and statistical/other machine learning. For deep learning, algorithms are applied that allow for multiple layers of learning more and more complex representations of data. For statistical/other machine learning, statistical algorithms and algorithms based on other techniques are applied to help machines estimate functions from learned examples.

Essentially, machine learning allows computers to train by building a mathematical model based on one or more data sets. Then those computers are scored when they may make predictions based on the available data. So when should you apply machine learning?

There are a number of times when applying machine learning can give you a competitive advantage. Some prominent examples include:

  • When there is no available human expertise on a subject. Recent navigation to Pluto relied on machine learning, as there was no human expertise on this course.
  • When humans cannot explain their abilities or expertise. How do you recognize someone’s voice? Speech recognition is a deep-seated skill, but there are so many factors in play that you cannot say why or how you recognize someone’s voice.
  • When solutions change over time. Early in a rush-hour commute,the drive is clear. An hour later, there’s a wreck, the freeway comes to a standstill, and side streets become more congested as well. The best route to getting to work on time changes by the minute.
  • When solutions vary from one case to another. Every medical case is different. Patients have allergies to medications, multiple symptoms, family histories of certain diseases, etc. Solutions must be found on an individual basis.

These are just a few of the uses that you’ll find across industries and institutions for machine learning. Not only is the demand for machine learning growing, though, but there is now an evolving ecosystem of software dedicated to furthering machine learning and giving businesses and organizations the benefits of instantaneous, predictive analytics.

An evolving ecosystem of machine learning software.*

In this ecosystem, Intel is the most widely deployed platform for the purposes of machine learning. Intel® Xeon® and Intel® Xeon Phi™ CPUs provide the most competitive and cost efficient performance for most machine learning frameworks.

Challenges to Adoption of Machine Learning

There are a few barriers to adoption of machine learning that businesses need to overcome to take advantage of predictive analytics. These include:

  • Understanding how much data is necessary
  • Adapting and using current data sets
  • Hiring data scientists to create the best machine learning strategy for your business
  • Understanding potential needs for new infrastructure vs. using your existing infrastructure.

With the right machine learning strategy, the barriers to adoption are actually fairly low. And, when you consider the reduced TCO and increased efficiency throughout your business, you can see how the transition can pay for itself in very little time. As well, Intel is dedicated to establishing a developer and data science community to exchange thought leadership ideas across disciplines of advanced analytics. Through these articles and information exchanges, we hope to further help businesses and organizations understand the power of predictive analytics and machine learning”.

What is your opinion and how do you apply data analytics and machine learning? Let us know what you think.

About the Authors:

Nidhi Chappell is the director of machine learning strategy at Intel Corporation. Connect with Nidhi on LinkedIn and Twitter to find out more about how machine learning can give your business a competitive edge.

 

Ronald van Loon is director at Adversitement. If you would like to read more from Ronald van Loon on the possibilities of Big Data please click ‘Follow’ and connect on LinkedIn and Twitter.

 

 

Intel, the Intel logo, Intel Xeon, and Intel Xeon Phi are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others. © Intel Corporation.

Sources:
[1] http://www.bain.com/publications/articles/big_data_the_organizational_challenge.aspx
[2] http://www.mkomo.com/cost-per-gigabyte-update

Source: Machine Learning Becomes Mainstream: How to Increase Your Competitive Advantage

How You Can Improve Customer Experience With Fast Data Analytics

In today’s constantly connected world, customers expect more than ever before from the companies they do business with. With the emergence of big data, businesses have been able to better meet and exceed customer expectations thanks to analytics and data science. However, the role of data in your business’ success doesn’t end with big data – now you can take your data mining and analytics to the next level to improve customer service and your business’ overall customer experience faster than you ever thought possible.

Fast data is basically the next step for analysis and application of large data sets (big data). With fast data, big data analytics can be applied to smaller data sets in real time to solve a number of problems for businesses across multiple industries. The goal of fast data analytics services is to mine raw data in real time and provide actionable information that businesses can use to improve their customer experience.

Fast data analytics allows you to
turn raw data into actionable insights instantly

Connect with Albert Mavashev
Co-author, CTO & Evangelist at jKool

Analyze Streaming Data with Ease

The Internet of Things (IoT) is growing at an incredible rate. People are using their phones and tablets to connect to their home thermostats, security systems, fitness trackers, and numerous other things to make their lives easier and more streamlined. Thanks to all of these connected devices, there is more raw data available to organizations about their customers, products, and their overall performance than ever before; and that data is constantly streaming.

With big data, you could expect to take advantage of at least some of that machine data, but there was still an expected lag in analysis and visualization to give you useable information from the raw data. Basically, fast data analytics allows you to turn raw data into actionable insights instantly.

With fast data analytics services, businesses in the finance, energy, retail, government, technology, and managed services sectors may create a more streamlined process for marketing strategies, customer service implementation, and much more. If your business has an application or sells a product that connects to mobile devices through an application, you can see almost immediate improvements in how your customers see you and interact with your business, all thanks to fast data analytics.

Consider a few real-world examples of how fast data analytics have helped companies across business sectors improve their performance.

A Financial Firm Monitors Flow of Business Transactions in Real-Time

The world of finance has always been fast-paced, and today a financial firm can have many millions of transactions each day. There’s no way to spare the time or effort to constantly search for breaks and/or delays in these transactions at every hour of the business day. However, with fast data analytics, they found that they could consistently monitor the flow of business throughout the day, including monitoring of specific flow segments, as well as complete transactions.

With the right fast data analytics service, the firm was able to come to a proactive solution in which they could monitor the production environment using their monitoring software’s automated algorithms to keep a constant eye on transaction times. The software’s algorithms determined whether transaction flows were within acceptable parameters or if something abnormal had occurred, giving the firm the ability to respond immediately to any problems or abnormalities to improve their customer experience and satisfaction.

Monitored transaction flows using jKool data analytics solution

A Large Insurance Firm Ensures Faster Claim Processing

In another case, a large health insurance provider with over three million clients was in the process of a massive expansion. As the firm expanded, though, they noticed a disturbing trend. Over the span of a single month, the average processing time for claim payments had increased by a dramatic 10%, but only for a single type of transaction. While they had the tools necessary to analyze the operating system problems, the servers’ hardware, application servers, and other areas where the problem could be originating, they were dealing with monitoring tools that were half-a-decade old.

Thanks to these outdated monitoring tools, the insurance provider had a very expensive problem on their hands, as finding the solution was taking up over 90% of their tier-three personnel’s time and energy. Not only that, but customers were actually finding the majority of their application problems before the provider’s IT support could detect them.

To immediately diagnose the problem and get ahead of it, the insurance firm deployed a fast data monitoring service that immediately diagnosed what was causing the delays in claims processing transactions. The solution was found promptly – one claim type was sitting in a queue long enough that it would time out – and that the addition of new branch locations was causing over-saturation of their architecture design. By reconfiguring their middleware, they were able to accommodate the load increase and solve the problem without taking up valuable employee time and resources.

Just a few of the benefits of deploying this service were:

⦁ A 40% decrease in the mean-time-to-repair for the software problem.
⦁ A 60% decrease in the time spent by third-tier personnel to solve the problem.
⦁ A 35% decrease in the number of open tickets at help desk.
⦁ More than 30% improvement in the average processing time for claims.

A Securities Firm Ensures Dodd-Frank Compliance

Enacted in 2010, the Dodd-Frank Wall Street Reform and Consumer Protection Act is a US federal law that was enacted to regulate the financial industry and prevent serious financial crises from occurring in the future. Securities firms and other financial institutions must ensure that they are Dodd-Frank compliant in order to stay in business and avoid the risk of serious litigation. One such securities firm implemented fast data monitoring and analysis for Dodd-Frank compliance for all of their SWAP trades.

To be Dodd-Frank compliant, firms must report all SWAP trades “as soon as technologically possible”. Within a few minutes of the execution of a trade, a real-time message and a confirmation message must be reported, as well as primary economic terms (PET). If a message is rejected for any reason, it must be resubmitted and received within minutes of execution.

Without monitoring of their reporting systems, the securities firm found that they were in danger of being found non-compliant should anything go wrong within their internal processes. Fast data analytics solution gave them the real-time monitoring they needed to stay compliant.

How Can Fast Data Analytics Help Your Business?

As you can see from these examples, fast data analytics makes it possible for businesses to quickly turn raw machine data into actionable insights by tracking transactions, identifying issues with hardware and software, and reducing customer complaints. With the ability to identify and solve these issues faster and more efficiently, fast data analytics services can significantly improve any business’ customer experience.

These processes can all be monitored in real-time, giving you access useful analytics and insights for time-sensitive activities. Fast data analytics can help you stay compliant with government and/or industry regulations, avoid preventable losses and it improve your personnel’s efficiency by pinpointing errors and problems without taking up a lot of employees’ time and energy.

How do you want to use fast data analytics to improve your customer experience? Let us know your experiences!

Connect with the authors


Connect with co-author Albert Mavashev to learn more about the world of fast data and all that it can do for you
Co-author, CTO & Evangelist at jKool | Follow on Twitter


Connect with author Ronald van Loon to learn more about the possibilities of Big Data
Co-author, Director at Adversitement

 


Source: How You Can Improve Customer Experience With Fast Data Analytics | Ronald van Loon | LinkedIn