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In today’s world any organization which is not utilizing its data for gaining insights will fall behind its competitors. Data has become an important part of the working of an organization be it for monitoring, analyzing, or predicting requirements.
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We keep hearing about many organisations (expressing through various forms of communication) that Human Capital is their most important asset. While this narrative is very simple to communicate, the struggle in most organisations is to demonstrate the true value of people, through actions and results.
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When I first imagined the use of data in Automotive aftermarket, I painted a picture where the vehicle enters the service station, and the camera captures its registration number and then the system pulls out data from thousands of sensors in the vehicle to prepare a job card which tells which part or system of the vehicle needs attention. I am nowhere close to that as of today but it’s realistically possible.
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Modern organisations are massively transforming their finance function by leveraging data and applying advanced statistical techniques, machine learning algorithms.
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The transition to Spark from python can be quite challenging at first as we are introduced to a whole new set of terminology such as clusters, pool, mounts, spark API, and so on. In this post, we will look at how Microsoft Azure Databricks may be used.
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Apache Spark assists businesses in resolving data processing issues due to its quick data-processing capabilities. Any data processing program's performance is a crucial factor to consider while it's being developed. The following are the most important performance optimization strategies in Apache Spark.
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The need to adapt in a fast-paced business environment is constantly encouraging organizations to invest in new digital and analytics solutions. As soon as a business starts its digital transformation journey, numerous options are available especially with ready-to-deploy and off-the-shelf digital and analytics tools. However, each of these tools is built with a specific business context and it is paramount for any leading enterprise to rather custom build data products which suits their specific needs. Custom solutions are an expensive affair but obtaining commercial off-the-shelf software could do more harm than good in the long run if it is not suited to your objectives.
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A Problem… and being classified as “Positive”, we don’t do that very often, do we? While data science and analytics is the domain of the future with plenty of growth possibilities for employees and the organisations, there are plenty of challenges in terms of sourcing talent. Surprisingly, these challenges are a result of the accelerated growth opportunities this domain brings in.
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Mistakes are inevitable while learning a new tool. After all, that’s how you learn it. There is no learning without putting it into practice. But if you are a novice & are trying to deliver a Powerbi dashboard in the form of a report to the client, the following will save you a bunch of time.
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In the world of accounting and finance, we have been getting better at automating processes. These processes have helped us assess past performance with a few clicks of the trackpad of our laptops. If we think of what the next stage of evolution for us is, the answer lies within the question. The answer is ‘what is next?’
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Just getting the data into tables is not the goal of creating analytics projects, right? Each type of data can be best represented by a particular type of chart like a balance sheet that is best shown on a waterfall chart, cash burn on a line chart, etc. A Power BI consultant knows exactly which kind of chart is suitable for any information.
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Emphasizing with pride, the philosophy of Aays is “Employee First” and this philosophy is reflected in every decision and action across multiple dimensions. In turn, the employees’ philosophy is encouraged towards being a customer first. Overall, this model ensures that a career with Aays always has an assured positive impact.
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Aays is one of the best teams when it comes to culture, people-centricity, freedom to explore and express yourself. The team is driven by the passion to create something new every day. There are no judgments for any attempts which has not yielded the desired results. The focus is to learn, correct, aim again and achieve the intended goal.
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You might want to transform your data for a number of reasons. Raw data from source systems are not always meant for Analytics reporting. Generally, businesses want to transform
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Once you have pulled data via API, the next step is to decide on a cloud solution where this data will be extracted and store. There are many cloud solutions providers viz. Microsoft (Azure), Amazon (AWS), Google Cloud etc. Here, in this article, we will be discussing how we can use Microsoft Azure cloud to implement a Data Mart.
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After identifying the data sources, we next have to learn the mechanism to retrieve data from these sources. Data that is being generated on a daily basis makes it hard to track all the hidden insight. Let’s look at the steps to extract these data.
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The very first step is to get a high-level sketch of the FP&A Dashboard and determine the granularity of information needed. Let’s take an example; the above dashboard has key metrics viz. Revenue, COGS, SG&A, EBITDA which can be sliced by Departments, Months etc.
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The main consumer of any BI / Analytics solution is C-suites and the end goal is to get desired reports, dashboards and interactive visualizations which can drive critical decisions for their business. The important thing is to understand that Analytics is no more a Techie’s play and rather it should be simple yet provide powerful insights. However, there are several layers to a comprehensive BI solution which are not directly visible to Stakeholders.
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