How Big Data Analytics is Disrupting The Retail Industry

| April 20, 2018

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The retail industry is growing at a considerable pace! Isn’t that a term we have all heard? Let me tell you this, it is not growing at a considerable pace, it is freaking booming! Just to give you a sense of how it is booming – Sales in 2017 was clocked at USD 3.53 trillion, it is poised to grow at 4%. It is also on the cusp of a technology revolution. The power of mobile and digital technology has enabled retailers to craft cutting-edge experiences for their customers.

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VAM Systems Inc.

VAM Systems is a Business Consulting, Technology Solutions and Professional Services organization working with major organizations in USA, UAE, Qatar, Bahrain, India, Singapore and Australia.Delivers leading edge information and communication technology based business solutions to enable our clients to continuously stay ahead and achieve sustainable profit and consistent growth by leveraging new channels of customer engagement and service delivery as well as better and efficient employment of resources and processes with measurable parameters for performance. While our local presence assists us in better understanding of the local needs, our global presence assist us to offer solutions strengthened by experience in leading markets globally.

OTHER ARTICLES

Choosing External Data Sources: 4 Characteristics to Look For

Article | May 12, 2021

Decision-makers at consumer brands are finally realizing the full transformative potential of external data - but they’re also realizing how difficult it is to source. Forrester reports that 87% of decision-makers in data and analytics have implemented or are planning initiatives to source more external data. And those initiatives are growing outside of the IT team; 29% of those surveyed say that IT has primary ownership of data sourcing, down from 37% in 2016. To support these projects, organizations are increasingly turning to a new specialist: the data hunter, who identifies and vets external data sources. It’s a lot of work to build external data-focused teams, and many leaders are realizing that external data is difficult to scale as the source list grows. Perhaps that’s why 66% of those decision-makers surveyed by Forrester report that they’re using or planning to use external service providers for data, analytics, and insights.

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Do You Know the Differences Between Business Analytics and Data Analytics?

Article | May 12, 2021

There are some fundamental differences between Business Analytics and Data Analytics, though both hold their own importance. For example, to discover patterns and observations that are ultimately used to make informed organizational decisions, Data Analytics includes analyzing datasets. On the other hand, to make realistic, data-driven business decisions, Business Analytics focuses on evaluating different kinds of information and making improvements based on those decisions. In this blog, we discuss in more detail their individual benefits and areas of expertise. Data Analytics vs. Business Analytics attracts a lot of interest from budding analysts; we will take multiple factors into account and help explain the difference between data analyst and business analyst.

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BIG DATA MANAGEMENT

How Should Data Science Teams Deal with Operational Tasks?

Article | May 12, 2021

Introduction There are many articles explaining advanced methods on AI, Machine Learning or Reinforcement Learning. Yet, when it comes to real life, data scientists often have to deal with smaller, operational tasks, that are not necessarily at the edge of science, such as building simple SQL queries to generate lists of email addresses to target for CRM campaigns. In theory, these tasks should be assigned to someone more suited, such as Business Analysts or Data Analysts, but it is not always the case that the company has people dedicated specifically to those tasks, especially if it’s a smaller structure. In some cases, these activities might consume so much of our time that we don’t have much left for the stuff that matters, and might end up doing a less than optimal work in both. That said, how should we deal with those tasks? In one hand, not only we usually don’t like doing operational tasks, but they are also a bad use of an expensive professional. On the other hand, someone has to do them, and not everyone has the necessary SQL knowledge for it. Let’s see some ways in which you can deal with them in order to optimize your team’s time. Reduce The first and most obvious way of doing less operational tasks is by simply refusing to do them. I know it sounds harsh, and it might be impractical depending on your company and its hierarchy, but it’s worth trying it in some cases. By “refusing”, I mean questioning if that task is really necessary, and trying to find best ways of doing it. Let’s say that every month you have to prepare 3 different reports, for different areas, that contain similar information. You have managed to automate the SQL queries, but you still have to double check the results and eventually add/remove some information upon the user’s request or change something in the charts layout. In this example, you could see if all of the 3 different reports are necessary, or if you could adapt them so they become one report that you send to the 3 different users. Anyways, think of ways through which you can reduce the necessary time for those tasks or, ideally, stop performing them at all. Empower Sometimes it can pay to take the time to empower your users to perform some of those tasks themselves. If there is a specific team that demands most of the operational tasks, try encouraging them to use no-code tools, putting it in a way that they fell they will be more autonomous. You can either use already existing solutions or develop them in-house (this could be a great learning opportunity to develop your data scientists’ app-building skills). Automate If you notice it’s a task that you can’t get rid of and can’t delegate, then try to automate it as much as possible. For reports, try to migrate them to a data visualization tool such as Tableau or Google Data Studio and synchronize them with your database. If it’s related to ad hoc requests, try to make your SQL queries as flexible as possible, with variable dates and names, so that you don’t have to re-write them every time. Organize Especially when you are a manager, you have to prioritize, so you and your team don’t get drowned in the endless operational tasks. In order to do this, set aside one or two days in your week which you will assign to that kind of work, and don’t look at it in the remaining 3–4 days. To achieve this, you will have to adapt your workload by following the previous steps and also manage expectations by taking this smaller amount of work hours when setting deadlines. This also means explaining the paradigm shift to your internal clients, so they can adapt to these new deadlines. This step might require some internal politics, negotiating with your superiors and with other departments. Conclusion Once you have mapped all your operational activities, you start by eliminating as much as possible from your pipeline, first by getting rid of unnecessary activities for good, then by delegating them to the teams that request them. Then, whatever is left for you to do, you automate and organize, to make sure you are making time for the relevant work your team has to do. This way you make sure expensive employees’ time is being well spent, maximizing company’s profit.

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Self-supervised learning The plan to make deep learning data-efficient

Article | May 12, 2021

Despite the huge contributions of deep learning to the field of artificial intelligence, there’s something very wrong with it: It requires huge amounts of data. This is one thing that both the pioneers and critics of deep learning agree on. In fact, deep learning didn’t emerge as the leading AI technique until a few years ago because of the limited availability of useful data and the shortage of computing power to process that data.Reducing the data-dependency of deep learning is currently among the top priorities of AI researchers.

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Spotlight

VAM Systems Inc.

VAM Systems is a Business Consulting, Technology Solutions and Professional Services organization working with major organizations in USA, UAE, Qatar, Bahrain, India, Singapore and Australia.Delivers leading edge information and communication technology based business solutions to enable our clients to continuously stay ahead and achieve sustainable profit and consistent growth by leveraging new channels of customer engagement and service delivery as well as better and efficient employment of resources and processes with measurable parameters for performance. While our local presence assists us in better understanding of the local needs, our global presence assist us to offer solutions strengthened by experience in leading markets globally.

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