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How Can Retailers Harness Big Data?

Big data analytics are becoming increasingly indispensable across most industries, including retail. When retailers clearly understand their industry by leveraging key information, they can improve their marketing efforts, demand forecasting, inventory planning, and much more. However, those looking to big data for improvements must overcome certain challenges and develop specific strategies before implementing big data analytics into their businesses. With a range of potential opportunities, what are some business applications for big data in the retail sector?

What are retailers doing?

Retailers can leverage big data in several ways to gain an edge. Here are five key areas worth mentioning:

  1. Personalization — Many retailer websites feature recommendation engines that use customer preferences to tailor selection results. However, it can be challenging for these engines to make recommendations effectively without being obtrusive. For instance, generating irrelevant offers can irritate potential customers. Through big data analysis and machine-learning capabilities, retailers can use customer information to train their engines. For example, they can improve relevance by implementing a control loop that compares generated recommendations to response rates. In other words, retailers are using big data to improve personalization with increasing accuracy.
  2. Pricing — Product prices can fluctuate throughout the year, especially during high-demand periods. How do retailers adapt? Through big data analytics, retailers can employ dynamic pricing, a pricing method that automatically adjusts their prices in response to their competitors’. Retailers can use analytics software that monitors prices and creates rules so the software adjusts prices accordingly. However, if retailers don’t check or limit their dynamic pricing controls, they can become problematic; if a competitor drastically discounts a product and the retailer hasn’t set restrictions on his or her dynamic pricing system, it can devastate margins. Therefore, big data has the capacity to make real-time price matching easy and effective — but only if retailers set clear boundaries.

  3. Inventory — It’s important for any retailer to have a clear view of his or her inventory, especially if he or she oversees more than one location or channel. In the past, industry insiders relied on physical observations and manual inventorying. Now, a variety of digital touch points makes inventory management simpler, even for those with multiple channels. Big data analytics can help retailers track their inventories and understand trends, which, in turn, can help them better — and more accurately — prepare for changes in demand.
  4. Competition — As with inventory, previous competitor assessment methods were relatively low-tech. Retailers can do more than set competitor pricing alternatives based on real-time data: They can also use competitors’ success to their own advantages by monitoring which competing deals are the most profitable and localizing prices based on what competitors’ customers are buying.
  5. Sensor analytics — Through increasing Internet of Things (IoT) connectivity, retailers have more opportunities than ever to connect with and attract customers. This year, more retail owners will likely “profit from an increase in sensors and data coming from various customer-owned devices.” This, in turn, is generating greater interest among global retailers hoping to take advantage as 70% are “ready to adopt the Internet of Things to improve customer experiences.” By leveraging customer information on everything from loyalty cards to social media platforms and store apps, retailers can use these countless data points to gain an edge.

It’s clear that, when it comes to harnessing big data, there’s plenty of room for retail success. To truly unlock big data’s potential, retailers will likely invest in more IoT, machine learning, and automation technology. However, careful strategic planning and preparation are essential for any business leader planning to tap into big data. Retail executives should understand what their goals are for leveraging big data, what information they will need, and how they will turn insights into actions moving forward. Without proper planning, it is easy to become overwhelmed — and more information can hinder rather than help business operations. Data has always driven decisions: Big data technology is exciting because it drives smarter, faster, more accurate decisions based on real-time information from many, many data points.

The experts at Value Global understand the retail landscape and offer many customized IT solutions. If you’re looking to leverage smarter ways of doing better business, visit Value Global online or give us a call at 281.713.9895.

What Top 2018 Tech Trends Could Impact Your Business?

We’ve hardly scratched the surface of 2018, but it’s never too early to anticipate and prepare for technology trends and developments promising to shape the future of managed services — and the world. Although many of these developments pose beneficial opportunities for tech company leaders, they may also present challenges to those not ready for impending industry changes.

Here are the biggest trends of 2018 you need to know about and how they may impact your business.

Artificial intelligence and machine learning

It’s no secret implementing smarter, faster programs into company workflows can offer sweeping benefits. From accurate, adaptable analytics to automation and beyond, some major disruptive developments entail artificial intelligence (AI) and machine learning. Gartner researchers predict having the ability to use AI will “reinvent business models” and user experiences over the next several years. Although virtual assistants like Alexa are mainstreaming AI, experts agree the greatest changes will occur through implementing narrow AI, which utilizes machine learning to fulfill specific, data-intensive tasks. For companies whose leaders utilize this technology, this means advances in predictive decision-making and augmented analytics, which use machine learning to prepare data and discover deep insights for a wide range of solutions.

Smart devices and the Internet of Things (IoT)


As with AI, advances in IoT applications promise to revolutionize analytics and networking. By increasingly and constantly connecting billions of IoT devices, business insiders are making leaps in data management and service automation. Perhaps more important, the amount of information and speed of analysis IoT offers is enabling many business professionals to work more efficiently and profitably than before. For example, truck manufacturers reduced their 180,000-truck fleet management costs by 80% thanks to versatile datasets they created using connected technology. IoT implementation benefits have prompted many industry power players, such as those at Microsoft, to invest heavily in IoT analytics, and savvy tech leaders are following suit.

Augmented world

augmented reality

It has become exceedingly clear in the last few years that virtual reality is useful for more than just video games. The technology underlying immersive digital worlds has disrupted many areas from user experiences to assistive technologies. Many major developments in augmented reality (AR), which provides visual overlays on physical objects, are proving their worth in the business arena. Gartner researchers predict that “mixed reality,” a technology that combines digital and real-world objects to create a virtual user experience while keeping users rooted in the present, is a key aspect of AR worth watching over the next five years.

Another AR-adjacent trend is the continued development of conversational platforms. Thanks to advances in AI and machine learning as well as AR, some company leaders have turned to chatbots for automating and streamlining their customer service interactions. Early conversational technology implementations were limited to highly structured, simplistic answers. However, according to the Gartner article, these platforms will likely evolve in the coming years as they become able to incorporate complex vocal and even visual data to complete user requests. More robust conversational technology stands to benefit those whose jobs span multiple industries by delivering advanced user-friendly interactive experiences.

Cloud services

Cloud services are becoming an obvious choice for many company leaders looking to streamline business operations. As a result, more and more business decision-makers are turning to MSPs, helping them to better understand, implement, and manage their cloud-based services with the added benefit of ongoing support. As a means to save costs, using public clouds is also becoming a go-to option for many. This, in turn, is driving an even greater demand for hybrid clouds. And as more business professionals better understand hybrid cloud advantages, flexibility, and value, many dub 2018 the “year of the hybrid cloud.” Based on increasing successes and demand, it’s likely cloud platforms will remain a force used for consolidating or eliminating antiquated on-premises business systems.

2018 should be an exciting year as many business processes are ripe for disruption as more and more company leaders recognize the benefits behind these technologies. Many are shifting their mindsets from the notion that these are simply buzzwords to understanding, realizing, and utilizing their value. Smart company leaders are already taking steps to corner these emerging technologies, working to innovate and increase value for their customers.

For more on the latest trends and developments shaping managed services and disrupting businesses, or to learn more about innovative managed services solutions, visit Value Global online.
Information Technology

Artificial Intelligence Is Making Automated Testing Faster and Better than Ever

In case you haven’t noticed, we’re in the midst of an artificial intelligence (AI) revolution — but not the scary Hollywood-movie kind, of course. Rather, AI is emerging with disruptive technologies like self-driving cars and mobile assistants, quickly leaving the realm of science fiction and entering into our everyday lives. Such disruptions already extend to the software development sphere and, as developers continue implementing and innovating with AI and lightning-fast software, the time will surely come for testers and developers alike to adapt.

When we think of software testing, we tend to picture a rigorous and often mind-numbing process hard on quality assurance (QA) professionals’ fingertips — and even harder on developers’ wallets. According to a report, researchers estimate developers still perform 90% of testing manually at a price tag of $70 billion, requiring two billion human-hours. And many test automation tools implemented throughout the last decade rely on virtually the same outdated workflows as manual testing without any substantial gains.

Software testing is known to take up significant time and resources. And successful advances with AI and machine learning in other industries make automation and utilization in the testing domain a no-brainer.

Automating software testing with AI

Software testing

AI-driven testing (AIDT) development and implementation allows users to leverage machine learning and smart algorithms to rapidly generate and run thousands of test scripts to report functional, performance, and security-related results. Since early 2017, leaders of several large companies have incorporated AIDT into their testing workflows. They’ve made vast improvements compared to the work traditional QA engineers produce.

Some notable strides include the following:

  • Test coverage has grown from about 50% to over 90%.
  • Scripting speeds have increased as “AI can generate 1000 scripts in a few seconds versus 3.6 million seconds for humans.”
  • Real-user representations are much more attainable.
  • False positives are “almost nonexistent.”

The goal for those developing — and using — AIDT is to reduce false-positive rates, improve efficiency, lower costs, and increase productivity. By outsourcing testing efforts to AI software, QA professionals and developers alike have more time to focus on analyzing and improving product quality. The financial savings are equally far-reaching. According to the eBook, IBM report researchers estimated bugs QA professionals discover can cost $1,500, which can rise to over $10,000 or more plus damage to company reputations if end-users discover them, compared to only $100 for bugs found early in development. AIDT productivity gains could result in millions or even billions in savings, depending on company size.

The future looks bright — and far more efficient

Software testingPrevious automated testing generations laid the groundwork for today’s AIDT systems; however, their script-by-script workflows have gone mostly unchanged over decades. This means QA engineers still create and debug scripts at slow rates relative to applications’ increasing complexities.

Combine that with human error and you have workflows that simply cannot compete with AIDT in terms of accuracy and speed. Those who continue to incorporate AI into their products, services, and systems will need to change their operations to match.

Consider the current user analytics approach. QA testers traditionally mimic user behavior based more on business analysts’ assumptions than on real user data. A thorough user behavior analysis would require immense humanpower and countless permutations — making AIDT even more attractive. AI-driven software utilizes different metrics than traditional software QA engineers might use. Deep neural networks in many systems allow software to assess vast quantities of real user data and identify potential errors based on the low-level features their algorithms detect. In other words, when it comes to representing real users, AIDT software can better predict as well as outwit and outperform existing systems with impressive accuracy by using self-learning algorithms.

AI-driven software testing may be new to many, but its benefits are already noteworthy. It’s proven to attain high test coverage with ease while simultaneously driving agile development operations. Its efficiency and accuracy — thanks in large part to its machine learning implementation — make it a cost- and time-saving measure that disrupts dated operations.

Interested in learning how automated testing can improve your business? Contact us today!
Double exposure of women Engineer in hipster shirt working with tablet in control room of oil and gas platform or plant industrial for monitor process, business and industry concept
Information Technology, Technology

Automated, Accelerated, Applied: How Machine Learning Can Improve Your Managed Services

For any company that relies on managed service providers (MSPs) to automate IT services and streamline operations, there are no two words perhaps more important to their future than machine learning. Machine Learning is the intelligent interpretation of high volumes of data by machines to make improvements without being explicitly programmed.

Although machine learning is not a new concept to data scientists, the term has taken the business world by storm in the last several years, thanks to its increasingly wide range of applications. In fact, a Forrester brief states that machine learning investments should increase by more than 300% this year compared to 2016. As machine learning becomes more common for MSPs and used within more applications, it will become more fundamental to the success of any emerging business.

Traditionally, managed services collect large volumes of data from their monitoring services. The data is collected, reviewed, and acted upon by the service provider. As the volume of data scales up, it becomes increasingly challenging, if not impossible, to have eyes reviewing all the data being generated. This is where automated workflows can be highly effective in improving existing managed service solutions. Those quick to adapt are already seeing that machine learning serves to accelerate these improvements — but how?

Machine learning 101

Brain with printed circuit board (PCB) design and businessman representing artificial intelligence (AI), data mining, machine learning and another modern computer technologies concepts.

Like other innovations, such as the computer and the steam engine, machine learning is known as a foundational technology — that is, one whose applications grow both horizontally and vertically beyond its initial intended use. As the Entrepreneur article points out, those applications include self-driving cars, music recommendations, and personalized ads.

Some of these processes may seem like the work of pure magic; however, in reality, they’re  highly advanced and complex. This technology operates via a facet called deep learning. In short, a computer uses unstructured data — such as pictures, sounds, and behaviors — and draws conclusions based on rules of its own design. This differs from the logic found in traditional programs in that a machine learning program operates autonomously and intuitively.

virtual human Machine learning helps to quickly analyze the data and then recommends a set of actions. For example, existing data collected from monitoring is analyzed for trends, patterns, or issues previously faced to identify actions that can be implemented. The ability to use existing customer data to generate a prediction model and identify an optimal algorithm can, in turn, improve a MSP user’s ability to locate and attract new customers. Where users once had to sift through millions of rows the algorithm goes through based on patterns and shortlist data elements, they can now look at a very rapid pace and drive actions.

Currently, most MSPs use automated workflows that they create themselves and program into the systems, but machine learning would eliminate the need for a person to do that. Thus, this high-performing interpretation helps to automate IT operations on a greater scale than ever before.

Smarter data integrity

Server racks in server room data center. 3d render

Forward-thinking and the adaptation of such technologies is important to both MSPs and their clients. Incorporating a more advanced level of predictive analysis and automation can enhance proactive monitoring and reduce the level of effort required of consultants. While MSPs help to streamline a business through managing IT complexities, machine learning optimizes the functions of these complex systems, creating a win-win.

For Value Global clients, machine learning means improved data analytics, which translate to better levels of service and a more efficient organization. The intricate data sets these systems build ultimately allow users to gain new insights and improve existing services. And the ability to fast-track the evolution of managed services can save both time and money, while also giving users a competitive advantage.

As machine learning becomes more common in technology and, more importantly, in business, clients will continue to demand more intensive and intelligent IT services. Thankfully, the organic, foundational nature of machine learning offers an optimistic image for the future of managed services. All it takes is the desire to innovate — and a little clever programming.

When it comes to managed services and intelligent IT solutions, Value Global is committed to innovation. Contact us for more information about machine learning, or visit us online to learn more about our great services.
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