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Search and big data analytics have evolved significantly over the last few years, and organizations are increasingly using these technologies to meet their mission-critical needs.

At the beginning of 2016, we were talking a lot about machine learning and semantic search and how they would be key developments in this space. 

Those have certainly been hot topics and continue to be areas that companies are seeking to exploit for their data-driven applications. 

But what will we be talking about in 2017 as it pertains to this space?

Here’s a look at five areas you can expect to hear more of this year and beyond.

Open Source Rises to the Top

Open source technologies are becoming more prominent in a wide range of use cases, from traditional enterprise search to log analytics, e-commerce search, and even government document search.

 

In fact, current data shows open source search engines have gained significant popularity because of flexibility, cost and features. According to DB-Engines, Elasticsearch and Solr — two open source search engines based on Lucene — top the list of leading commercial and open source search engines.

Just last month I discussed the features and limitations of Elasticsearch and Solr in this article.

With its growing use in the commercial and government space, the move to open source will continue to be a hot topic as organizations seek greater features, costs savings, and more flexibility with their search and big data analytics solutions.

Life Without Google Search Appliance

In early 2016, Google announced its end of support for the Google Search Appliance (by March 2019) as part of its strategic move to a cloud-based platform. Since then, many have begged the question: What now?

In my article last summer, I provided some tips on moving from the GSA and looked at some of the replacement options available at the time.

As we enter 2017, there are still no concrete details from Google about a new cloud-based search solution, so users are forging ahead with seeking out and comparing their existing alternatives.  With the March 2019 deadline getting closer, we’ll be hearing a lot more about the alternatives, and maybe even get word on Google’s cloud-based plans. 

Analytics Powered by Enterprise Data Lakes

Enterprises have a lot of data but how well they use it to derive insights is key to success. Over the last year, we’ve been hearing a lot of hype around enterprise data lakes (or enterprise data hubs) to bring together data silos and make the right data available to the right users at the right time. 

There is a wide variety of structured and unstructured data in enterprise data lakes. That said, search engines are the ideal tool for storing, processing, accessing, and presenting this data because they are schema-free and can scale to billions of records.

Data lakes’ search and analytics capabilities are nearly endless when we combine search engines, big data techniques, and visualization dashboards in groundbreaking use cases such as bioinformatics, precision agriculture, and precision medicine.

As data lakes continue to gain popularity as a way to store massive amounts of data and analytics, we’ll see organizations continuing to have the conversation in 2017 about how to best exploit this.

Search Engines Become 'Insight Engines'

Just like Google, Cortana and Siri, search is becoming much more than just keyword matching.

We’re now heading into the age of search results personalization. Search engines are becoming personal digital assistants or as Gartner calls it, Insight Engines. This was made possible with big data analytics techniques like machine learning and predictive analytics.

It’s pervading the modern business world in a multitude of use cases from intranet search, to e-commerce search, recruiting, medical research, media and publishing, and many others. Organizations are just beginning to skim the surface on how real-time personalization is significantly enhancing their operations so we expect a lot of talk about how to implement this in the coming year.

Search Engine Scoring

We know that analyzing statistically valid scores helps increase search engine relevancy over time. But, while this method can significantly increase business value and the bottom line, not many organizations have started engine scoring or are implementing it effectively.

With that said, we are observing proven success with newer, better algorithms used in the scoring process, some of which are discussed in this article.  This is, without a doubt, a solid technique that will continue to grow and be discussed in the coming years as organizations seek to improve their user’s search experience.

In conclusion, with the rise of open source, massive volumes of structured and unstructured data, and the need to do complex analytics, we will continue to hear a lot on these topics throughout 2017 as search and big data continue to converge. 

About the Author

Kamran Khan is co-founder, president and CEO of Search Technologies, a company dedicated to developing, implementing and supporting search and big data solutions. He has been developing, supporting, selling and managing in the computer software/services industry for 25 years with a focus on search engine technology.

Author : Kamran Khan

Source : http://www.cmswire.com/big-data/search-big-data-analytics-in-2017-5-hot-topics/

Categorized in Internet Technology

If there's a word that describes the retail space in 2016, it's change. Change in technology, tools and best practices. And, (no surprise), 2017 promises more of same.

Here are five trends destined to make retailing more effective and profitable in 2017.

Multi-channel data integration

After using data analytics for several years, retailers are getting a clear idea of the benefits that high-volume, high-speed data analytics can provide. Unlimited computing capacity in the cloud and advanced analytics enable retailers to overcome a familiar challenge: collecting and analyzing huge volumes of different types of data (databases, social media and instant messages, reports).

More recent developments show by using data analytics software, retailers can unify online and offline data by:

  • Extracting data from different places such as legacy systems and database platforms on-premises or in the cloud.
  • Using new sources of data from commerce, supply chain and customer channels.
  • Integrating conventional retail information and data from new channels with company ERP, order management and warehousing software.
  • Delivering useful operations suggestions quickly enough to capture business opportunities as they occur. Modern data analytics software can cut the time from weeks to minutes.

Modern retail analytics software packages customer and supply chain data and trends in a single view of what's going on. Putting all relevant data into a form that's easy to understand and use helps business users set up operational and promotional strategies and continue to improve efficiency and performance.

 

Predictive data analytics

Every retailer wants to have the right products available to customers at the right place and time. Making this happen, however, is not an easy matter.

Data analytics provides retailers with a better understanding of their current business.Predictive analytics provides retailers with a look into the future.

Until recently, retailers had to rely on insights gained from their own experience and retailing skill, analyst forecasts and customer feedback. But it all added up to high-quality educated guessing.

Predictive analytics uses mountains of data, which retailers already have, and a wide array of technologies and approaches (statistical modeling, data mining and other techniques) to analyze and project the likely outcome of future events and consumer behavior.

The biggest business value of predictive analytics is its ability to help retailers stay ahead of the expectations of discerning, tech-savvy consumers. This includes:

  • Delivering a better shopping experience. That is, enabling customers to shop whenever and wherever they want in an attractive, no-worries environment, in the store or online.
  • Getting a clearer view of customers. This includes a 360-degree view of customers and click-stream analysis.
  • Merchandizing and planning. Add real-time promotions, demand forecasting, pricing and markdown optimization and out-of-stock analysis and management.

One of the biggest changes in retail analytics lies in where all this data comes from.

 

Internet of Things in retail

Pioneering major retailers are scrambling to collect and analyze data from the Internet of Things. Customers provide useful IoT data by using and connecting to smartphones, tablets and wearables. Brick-and-mortar stores use IoT data generated by digital signage and other in-store sensors and devices.

Together, these sources generate massive data stores that describe customer behavior. Retailers use this data to make decisions and create sales strategies for their brick and mortar stores and distribution centers.

Innovative uses of IoT data and technology enable retailers to:

  • Customize a shopper's in-store experience. Increasingly, customers expect personalized service. Data collected from in-store IoT devices and the shopping history of connected consumers enable retailers to create a shopping profile of each customer. IoT data analysis discovers shopping patterns that help retailers deliver a more customized shopping experience.
  • Make in-store operations more efficient. Data harvested from in-store, IoT-enabled smart cameras, beacons, and sensors provide store managers and employees with a deeper understanding of what does and doesn’t work well on the floor. For example, analysis of real-time location datafrom smartphone apps can be transformed into customer traffic patterns and buying behaviors. With this information, employees can be alerted to bottlenecks immediately and reduce customer wait times at the cashiers.
  • Improve inventory and supply chain management. Smart transportation management applications and demand-aware warehouse fulfillment are two ways to transform IoT data to into an understanding of what’s underperforming, overstocked or running out of stock at your store.
  • Take advantage of new revenue opportunities: Leading-edge retailers are using the IoT to find new methods of acquiring customers and increasing revenues. For example, beacons and Wi-Fi can create an in-store environment, in which customers engage in contests, meet-and-greet events and social media product reviews.

Self-service analytics software  

Not long ago, data analytics software users had to wait for reports designed and delivered by data analyst middlemen. When customers lobbied vendors for change, they got results. Business users got self-service applications that included easy-to-use dashboards and enabled direct queries. The software empowered business users to ask relevant questions and get answers—quickly—without data science degrees.

Specialized retail analytics software enables store managers and retail decision makers to:

  • Use easy-to-understand analytics methods on data relevant to their store.
  • Easily access, explore, and analyze data with just a few clicks
  • Quickly and easily engage with supply chain data.
  • Make decisions by analyzing products and merchandising methods.
  • Identify spending patterns and gain insight into customer behavior by choosing from a library of interactive visualizations.

Mobile to the rescue

We’ve all heard the complaint that customers enter brick-and-mortar stores with more product information than the staff. Equipping staff members with mobile devices linked to key internal applications and databases enables associates to personalize customer services and perform "save the sale" rescues with pricing, promotion and product information.

Author:  Ilan Hertz

Source:  http://www.retailcustomerexperience.com/blogs/data-analytics-and-the-changing-world-of-retail-in-2017

Categorized in Search Engine

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