Data Analysis is the New Elixir for Marketing Strategies

Data analysis is treated as the last straw and is the solely effective and efficient coping mechanism for businesses and markets that were beaten out of shape by the pandemic. An alteration in supply chains and productions, a change in consumer behaviour during and post pandemic had put marketers in a soup because not only the production methods but the delivery methods had to be altered as well. Simplicities were met with complications.

The scenario has not changed and for this reason alone, marketers are vehemently taking to data analytics in order to deal with the avalanche of data that keep mounting one above the other. Besides reportedly, business organisation employing data analyses are likely to have their data in order and carry out business functions flawlessly.

Tough times that have taught us about Digitalisation

The times in which people are locked away in their houses, unable to step out, the digital medium are the one that are keeping us connected.

Anil Bhatia highlights the importance of digital transformation of business organisations and have praised the ones who are taking digital transformation seriously. It is important for business to inculcate digitalisation in their infrastructures so that the human workforce is not required on field and that machines are enough to carry out tasks on their behalf. This is one wise way to ensure prevention from the virus.

One cannot talk in absolutes when it comes to marketing strategies. Strategies are always in a state of flux and decisions are made on what rocks the boat of and effective market. Owing to the same, businessmen and organisations are adapting to digitalisation to meet their desired ends.

#big data #latest news #data analysis #data analysis is the new elixir for marketing strategies #new elixir for marketing strategies

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Data Analysis is the New Elixir for Marketing Strategies

Data analysis is treated as the last straw and is the solely effective and efficient coping mechanism for businesses and markets that were beaten out of shape by the pandemic. An alteration in supply chains and productions, a change in consumer behaviour during and post pandemic had put marketers in a soup because not only the production methods but the delivery methods had to be altered as well. Simplicities were met with complications.

The scenario has not changed and for this reason alone, marketers are vehemently taking to data analytics in order to deal with the avalanche of data that keep mounting one above the other. Besides reportedly, business organisation employing data analyses are likely to have their data in order and carry out business functions flawlessly.

Tough times that have taught us about Digitalisation

The times in which people are locked away in their houses, unable to step out, the digital medium are the one that are keeping us connected.

Anil Bhatia highlights the importance of digital transformation of business organisations and have praised the ones who are taking digital transformation seriously. It is important for business to inculcate digitalisation in their infrastructures so that the human workforce is not required on field and that machines are enough to carry out tasks on their behalf. This is one wise way to ensure prevention from the virus.

One cannot talk in absolutes when it comes to marketing strategies. Strategies are always in a state of flux and decisions are made on what rocks the boat of and effective market. Owing to the same, businessmen and organisations are adapting to digitalisation to meet their desired ends.

#big data #latest news #data analysis #data analysis is the new elixir for marketing strategies #new elixir for marketing strategies

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Data Is The New Marketing Currency

Brands today are differentiated by how well they know their customers and craft campaigns that are intuitive and useful for the end-customer. Data does exactly that and enhances the efficacy of marketing campaigns. Data-led insights give us the liberty to investigate the diversity of user behaviour and understand our customer base more intimately. The barrage of data created online every day is amplifying at a magnificent rate. The lack of an effective tool can confound marketers and even diminish the data’s utility because of failed attempts to integrate the widespread information.

Sitting on a deluge of fragmented, disconnected data pointers is counterproductive to growth. Marketers are losing sleep trying to figure out ways to help their businesses create a single source of truth. The solution is to decentralize data silos, make them accessible to multiple business users and enable marketing teams to create engaging marketing campaigns.

The need for an intelligent CDP is urgent

The rise of Customer Data platforms has been sharp, thanks to modern marketing solutions that rely heavily on data-driven campaigns. A CDP acts as a singular storefront for data residing on multiple fronts (CRM, legacy marketing solutions, analytics platform, DMP). CDP pools customer data into one place for marketers to get a 360-degree view at any given point in time. This helps marketing and product teams rely on a single source of truth for understanding consumer behaviour.

There are several solutions out there that will help you assimilate your data to create a data warehouse, data lake, or similar sounding fancy names. But, this isn’t enough to help you scale your marketing and engagement efforts. A CDP is essentially incomplete without the ability to govern data, create dynamic segments and use this data seamlessly in campaigns, and lastly, creating up-to-date profiles for users: known and unknown.

An efficient CDP will facilitate data mobility, pull data from multiple sources and send it to other platforms. The consolidation of user-profiles will help track the whereabouts of known and unknown users. Marketers then will be able to act on this and design a relevant communication strategy for the users.

All the data is then used intelligently to create small customer segments, which is leveraged across omnichannel campaigns. This allows marketers to deep dive into multiple use cases in a customer lifecycle. Context is King! This adage summarizes the final step, insights. The incredible insights from intelligent campaigns contribute to faster decision-making and carve out marketers’ next steps.

#opinions #data for marketers #data for marketing #marketers #marketing #data

How Are Data analysis and Data science Different From Each Other

With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.

Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.

Now, addressing the main topic of interest – how are data analysis and data science different from each other.

As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –

• Building/collecting data
• Cleaning/filtering data
• Organizing data

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Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management