Analytics- Solving business problems and career opportunity

by Sunil Upreti Digital Marketing Executive (SEO)

Analytics- solving business problems and creating opportunities

Organisations like Google and Facebook are iconic when it comes to using data for driving business. The popularity Business Analytics Courses in Delhi has gained over the years is a prove of that almost all companies are using this. Although a great deal can be said concerning their practices regarding issues such as privacy, ownership and governance, it can not be denied that both technical and cultural aspects have been pioneers in the field.

The mix is called DataOps by the people involved in building the technological infrastructure and the data-driven community Facebook uses. Big data were not what it is today when they began in 2007. All four Vs, which describe large data– size, variety, pace and truthfulness– were at lower levels.

More importantly, however, there has not been much experience with Big Data and its use to promote decision-making in organisations. At the time, the question remained whether it was useful to have all these data. Today, the sense is that data value has been demonstrated, and it is more a matter of how it can be obtained. Business Analytics institute in Delhi focus hugely on the importance this field has in the decision-making policies of organisations today.

To quote one of the pioneers of data-driven decision making, O'Reilly's Paco Nathan, “Decision makers are used to making judgments. Any CEO understands statistics at a gut level, because that's what they do every day. They may not know the math behind it, but the idea of collecting evidence, iterating on it and basing decisions on this is intuitive for executives.”

Things begin to happen, like extracting business ideas, from areas likely to be changed by putting in place the research needed to streamline access to information. There is a well-known story of how a Facebook intern analyses, how users interact, contributing to a global campaign, driving brand characteristics and development. There are however blunt evidences that points to a simple fact more than feeling and stories: data-based organisations perform better.

For example, according to a 2012 Economist study, companies dependent on data are more economically strong than their competitors.

The first step in this journey is to recognise the efficiency of decision-making based on data. Then there must be the correct facilities and the culture of organisations must also be changed and adapted. It's not all the new factors that make data-driven decisions. Nevertheless, the nature of the matter is different: The gathering of data and analytical quality was historically a privilege for few organisations, and it primarily evolved.

Business Intelligence (BI) and descriptive analytics are the concepts related to these types of methods and approaches. BI is something many managers have come to know about and even trust. This meant that large imprints were put on their desks in the beginning.

Such prints will outline key metrics and key KPIs such as output, revenue and churn. Despite administrators being used for studying such key figures, the practice went deeper and solved other issues. This knowledge could be too much and still not enough. This is there is an apparent focus on Business Analytics Courses in Delhi, as one needs to know all the tools and techniques before putting a leg in the field.

Imagine receiving metrics for an organisation with thousands of industries or staff. That would be intimidating and a lot of time and effort would be needed to search, let alone digest. KPIs are an organisation’s bird eye view, but are not necessary. Even if someone puts time and effort into measurements, what if they have found something that needs closer inspection?

How can you concentrate, for instance, on a particular branch that is underperforming, see historical statistics and compare them to the other branches? These are the types of problems that have led to the development of analytics.

We're now moving towards the solutions such as visualisations, dashboards and data warehouse from printed materials and ad hoc questions that dedicated teams need to process and collect data to answer.

As the questions became more complex, the questions needed to answer them became more difficult for both databases and teams. This leads to an establishment of a specific server category, the data storage facility, which is designed to respond to analytical questions.

Visualisation and dashboards were the other factor in advancing research. A number of graphs are introduced to provide an summary of huge amounts of data easily. Such maps are housed in dashboards, which function as a one-stop shop for organisational performance monitoring.

These dashboards were interactive at last. In practice, it meant that users would click on them and drill down to the underlying data, so that they could go from a view of the bird to the particulars of something that catches their eyes. One method of research is called clinical examination, which explains why something happened?!

The growth of NoSQL, Big Data and Hadoop Diagnostic analytics is extremely helpful for insights and companies are starting to develop and use information. Nonetheless, there are also disadvantages in this type of analysis, which slowly started to appear. The transfer of information from the operating servers to the data warehouses requires the complex, error-prone and time-consuming process of the collection, transformation and loading (ETL). Therefore, data warehouses are designed to respond to certain questions on the basis of pre-calculated dimension. All these complexities are taught and cleared in the best possible way during Business analytics classes in Delhi.

More ETL and data warehouse design and implementation cycles are expected as new questions arise. And, in order to make things worse, size, variety, speed and truth (4Vs) of information began to reach new heights that relation databases, which had been almost the only game in the region, had difficulties to sustain. The word big data, as well as a new range of technology, primarily Hadoop and NoSQL databases, were born.

NoSQL, which has finally been described as SQL, refers to a set of database solutions based on relationship models. Such (document, key value, columnar and graph) databases stem from the need of systems that scale functional applications and adapt models best to specific problems and domains. The servers must be assembled to different applications.

The issue, the traditional approach of functional relation databases and data warehouses, has not been tampered with more powerful hardware. NoSQL databases have been different. These are built to scale horizontally, so that the linear scalability is preserved by adding more nodes to a distributed, clustered network.

NoSQL has broken with the relationship database norm, building on the CAP theorem and eventual coherence. In the CAP theorem, one has to choose between reliability and accessibility in the presence of a network partition.

This means that most NoSQL bases depend instead on the BASE model (basically accessible, soft state and possible consistency) to reject the guaranteed consistency model of relational transactions ACID (atomic, stable, independent and durable transactions). In other words, the benefits of NoSQL databases may be, but circumstances do place a burden on the applications that preserve data integrity.

NoSQL also had an impact in analytics during its original targeting of operational applications. Yet Hadoop is the poster child of the Big Data era. Although Hadoop's design principles are similar to NoSQL, its emphasis is analytics. The basis of Hadoop is:

1. Hadoop uses a lot of hardware commodities nodes.

2. Have the main storage system distributed (HDFS).

3. Using the programming model (MapReduce) for efficient computation for defining the data position and parallelism.

This has resulted in several advantages:

1. Hadoop was cheaper than data centres.

2. Instead of structured connection data, Hadoop can save any kind of data.

3. Hadoop has also been fantastic at measuring and processing.

The latter needs to be examined further. The MapReduce model, together with the powerful storage and distribution of Hadoop's layered architecture, could be used to enforce all forms of processing, which effectively decouple the database architecture.

Therefore, Hadoop and the code depots also operate together and there is a flourishing Hadoop ecosystem. Most organisations use cost-effective data lake storage from Hadoop, which stores any type of data for subsequent processing. The ETL data can then be used in data centres in its programming context.

Big data, statistical analyses, machine learning and revolutionary AI Hadoop have had their problems. The ability to store any information at a reasonable price is good, but it was difficult to use the MapReduce system and needed knowledge which was hard to obtain. To address this problem, a prosperous ecosystem was built on Hadoop and around it and finally offers the ability of tools and even SQL interface to abstract MapReduce.

Analytical approaches can therefore again function in all kinds of backends– relational, NoSQL, and Hadoop. There was another problem, except in the case of Hadoop. Hadoop was developed as a batch processing tool and was used to pressure it outside of its intended purpose for interactive query. This too, as higher-level APIs and frameworks for Hadoop exist today.

It is, however, important to highlight the interplay of space, computer technology and analytics before going out to test the state of the art in Hadoop and beyond: Progression and demands in one move others forward. It is therefore worth a break for a while to see what is next for the Big Data Revolution. Amazing things are possible when you sit on mountains of big data. Can you remember the concept of “data as proof of decision support?”

When you dig further into this comparison between human decision-making and decision making based on data, imagine a seasoned specialist from any sector with plenty of experience and projects.

It could have been a ball player who seems to know how the next game will take place or a business analyst who seems to predict the contest. Often experts with this level of expertise and experience give the impression that they can foresee what will happen next. In reality, based on their experience, they make reasonable predictions or educated guesses. Predictive analytics are also concerned with this.

Predictive analytics involves the use of past data to predict the next. What are this month's sales going to be? Which clients are most likely to withdraw? What is likely to be cheating in transactions? How are you going to be a user? These are the types of questions to be answered by predictive analysis. These are very difficult questions. Even the determination of the parameters is difficult, let alone the way the parameters interact and generate algorithms to express this. Therefore, it is difficult to deal with these issues in a formal, programmatic manner.

But what if we can somehow recycle the time-honoured data to find trends which can be useful in predicting answers with relative accuracy to these questions? That, specifically, is the principle that fuels the emergence of predictive analytics behind Machine Learning (ML). Classes with robots are not a new approach. Although there have been advances in recent years, the bulk of machine learning methods are used for decades.

What has improved today is that we now have the information and computational power to work on this method. Machine learning is based upon and often computationally intensive data and research and human know-how. In the right ML algorithm, the data should be obtained, organised, labelled and linked. This process is called the algorithm learning, and it's a great art that some people can claim to master at this level. Yet the results could be impressive if they succeed. A wider range of fields, from fraud detection to medicine, with outcomes equal or greater than human experts, are being applied to ML.

As Nathan puts it:

"The competition is going to be about data, who has the best data to use. If you're still struggling to move data from one silo to another, it means you're behind at least two or three years. Better allocate resources now, because in five years there will already be the haves and have nots."

This is a strategic decision that contributes to strategic gains and transforms organisations. But what about those who, literally and metaphorically, can not or will not afford it? Not every organisation is digitally born. Not everyone can create a data team from day to day, and even if they want, at that stage there are just insufficient competent computer engineers and scientists. And, of course, it's an expensive company to build infrastructure. The cloud can therefore offer a solution. We apparently are also witnessing an aperture in the demand and supply of good analysts.

The plus and the downside of the cloud are now well established on the engineering front. The data provides elasticity with little to no upfront investment, and when data from apps that exist in the cloud are used to reverse and transfer data are less a concern. The supplier lock-in, on the other hand, is always important to remember. Moreover, the cloud has more to sell, apart from the computing infrastructure and cloud analytics software. No wonder we need good analysts for making our industries better and there’s no better way than Business Analytics Institute in Delhi.