Dataception

We’ve all been collecting customer data for years. But here’s the hard truth: Data is meaningless without a comprehensive, organization-wide strategy to consistently leverage it into actionable insights. While we might know what our customers are buying and how they’re buying it, without connecting the dots to understand customers’ path to purchase, we can’t be sure how to engage them to purchase again. It’s data’s potential to move beyond the transaction and demystify the full customer journey that makes it extraordinarily valuable – and then, only if we can leverage it. A solid and prescriptive data strategy can deliver the following business benefits: Increase revenue and drive down costs Solve complex business problems Drive customer insights and intimacy Lower business risks by predicting trends and customer behaviors Crystallize previously “invisible” emerging opportunities Support overall business strategy Data Strategy Fuels Business Outcomes It’s a compelling fact that is supp...

  Data is considered an asset. Data is been re-named into “ New-Oil ” of business houses. Data is the heartbeat of today’s business enterprises. We at Dataception , evaluate the real time fact on data and analytics use cases, features and functionalities that support business needs, as well as underlying data management and governance technologies that assist those use cases. We also checked the real time implementation hurdles. Out of our evaluation we think the data can either be a boon or a curse! The same asset- “data”- is holding back the potential innovation and process automation. Here is how and what is our frame work to overcome the hurdles. # Collection of Meaningful Data: There is huge volume of data. Every single transaction generates volume of data and the employees are often overwhelmed by this and they hate data! There are various channels producing data in various forms – structured and un-structured. To collate and create a meaningful data set is posing...

The field of decision science focuses on making data-informed decisions. Decision science helps to analyze the impact of a decision on the business. The best decisions are often made with a combination of data and precise business questions. The more precise the questions, the more precise the data requirements will be. Harvard’s Center for Health Decision Science (CHDS) explains that this unique science is a “collection of quantitative techniques” applied to decision-making at both the individual and population levels. It includes “decision analysis, risk analysis, cost-benefit and cost-effectiveness analysis, constrained optimization, simulation modeling, and behavioral decision theory.” Further, “decision science provides a unique framework for understanding public health problems.” However, decision science is not just applied to public health but also pricing decisions such as the optimal price for a product or service; product decisions such as measuring profitability vs. custome...

Now a days, data of an enterprise said to be an asset. It is termed as next “OIL” .  Since enterprises are digging for the data, it may be reason to name it! There are many process are driven by data!  Data is used to take decisions! Many more incidents and claims are happening in the business world.  What is reality? Or What is actually happening in this data world or world of data? Many brands invested into these tools and awaiting for the return on investment. ROI . Yes, data is truly big and happening word, however, it is not defined clearly! There are many pain points that should be addressed. There are many analytics options and one must put a big effort to choose. There are several technology related problems that we hear over and over from brands trying to leverage analytics to drive continuous, data-driven improvement across the enterprise.  In this post let us discuss the pain points (No specific order!) 1) Single way process. Frequently, analysis is undert...

While data-driven digital platforms and the recent pandemic have opened new and novel opportunities for businesses to go beyond geographical borders and compete in truly global business environments, an average business faces tremendous pressure to demonstrate the value of their operations. With the rising importance of data as the new oil of a global business environment, data analytics strategy has become a core component of most business operations.  Indeed, according to data industry expert and thought leader Bernard Marr, the International Institute for Analytics predicted that “by 2040, businesses using data would see $40 trillion in productivity benefits over competitors who are not using data.”  However, many businesses still do not have a cohesive strategy for implementing data analytics in their organizations. The effectiveness of enterprise data analytics functions can be measured via “outcomes,” for which businesses need to develop a plan known as a data analytics ...

Data analytics in businesses help uncover competitive intelligence, actionable insights and trends. Different types of analytics enable businesses to gain an edge over their competitors. In the “data first” era, data-driven insights and decisions have become the key drivers of business performance. The Raw Data for Different Types of Analytics Every business collects a vast range of data, namely sales data, supply chain data, customer data, employee (HR) data, transactional data, and much more. The data sources or channels can be numerous—sensors, applications, surveys, emails, or chats. The data type can be structured, semi-structured, or unstructured. Businesses have to rely on data analytics to make sense of huge piles of collected data. The raw data is collected, cleaned, and prepared, then analyzed for result-oriented outcomes. Data analytics can be applied to different business functions in different ways: predictions for the future; audience behaviour trends for marketing; quart...

Whereas analytics has a reputation for being rooted in the wizardry of  algorithms  and “pure math,” Southekal recast the practice as driven by finding the most relevant questions for a particular business and then using those questions – in conjunction with data – to gain insights. Without “visibility” into business goals and KPIs, analytics will not be valuable, he said: “A VP of an oil and gas company asked me, ‘Can machine learning and data analytics help me get more oil from the ground?’ The answer is no. To get more oil from the ground, you need a pump jack. But to get more value out of the pump jack, you need analytics, because it gives that kind of visibility into the factors that will help you get more value out of your pump jack, and ultimately get more value to the business.” Key Components of Analytics Southekal identified what he believes are the four building blocks of analytics: data, algorithms, ethics, and assumptions. “It’s not just about technology,” he expl...