Category: Analytics

There is a great paradigm shift, in the way data information management systems are perceived. And rightly so, as there are massive amounts of data being created every day. The data is moving sinuously in many directions and people want to access that data from all over places. The old methods of integration have become unsuitable in today’s time, and demands.
The age of Big data demands big changes. Around 80% of data is unstructured, we get this data from different sources and enterprises. Data in Applications and Cloud is also growing at a constant pace with about 20% of data being touched by the ‘Cloud’. In some sense, we live in a matrix of data and information, with an ever increasing source base and target base.
This advancement opens the pathway to new and refined approaches. One cannot use technologies based on extract, transform and load methods (ETL), as it becomes a bottleneck. One would need a massive amount of custom coding to use these old world tools. Even then, we would be left with a list of challenges and time-consuming activities like lack of documentation, very limited reusability, dedicated manual hours, giving rise to increased cost with low productivity.
We now know, that modern data is complex and more distributed, and if we plan to take advantage of this data, we need to plug the incoming data to modern data integration.
So that you don’t waste time in fighting the tide, there are some Principles of Modern Data Integration which help us with some new insights from the massive data matrix that surrounds us.

• Process data locally, place an agent locally, so the data is processed at the host platform before moving it. By doing this we can eliminate any bottlenecks.

• Modern data integration permits you to use powerful databases that already exist with built in functions to handle workflow, which can then be seamlessly blended, and distributed efficiently.

• Move data point-to-point rather than through data integration servers. Modern data integration allows you to move the data at the correct time and avoid bottlenecks.  Also if you do not wish to move all the data, but to keep up with time, on certain occasions you can process data in the natural environment and only move the resulting base. So it removes the bottleneck, creates network space, and increases the speed in which data is saved.

• For accessibility, transparency, and reusability, it is advisable to manage all the business rules and data logic centrally. Modern day data platforms can be managed centrally, avoiding the chaos of dispersed integrated applications.

• Since modern data integration, through metadata repository, allows central management of all business rules and data logic templates, changes with new data or migratory data, across platforms can be done through the already available rules and logic, in an efficient and effective manner.

Therefore, it can be said, that modern data integration will eradicate many challenges that old data integration brought on. Moreover, it will create new capabilities and opportunities much beyond our expectations.

There are a strong undercurrent and an emotion of turmoil Leverage in the IT sector over the last couple of months. The IT sector is facing a major upheaval, and the dust from the storm for Engineers and IT professionals seems to be far from settling down.
Yes, there have been some major layoffs by big industry giants. And it has created panic for young and mid- level employees. But panic is good, it helps you think forward and strategize on how to exist through evolution, panic helps you move from the ‘Why’ of a situation to the ‘How’ on resolution.
If one looks into the situation, it will be very clear that certain jobs and skills are becoming redundant or will be soon. The IT industry is not going through a turmoil but through a transition phase. And there are valid reasons for this change. It did not happen overnight and will not stop abruptly either.

Let us understand why this happened…

Advancement in IT Industry

Undoubtedly, these are uncertain times for the IT industry, but there is a logic to it. There has been some massive advancement in technology over the last couple of years in the field of IT services. It could be said that the growth in technology is happening at a more advanced stage and training/re-skilling initiatives by companies or individuals are not able to keep pace with it.

Change in US Policy

Another reason leading to this chaos is the US policy on tightening the norms to issue the H1-B visa, promoting job retention for the American people as opposed to outsourcing. This is definitely a blow to the Indian IT sector which hired extensively, about half of H1-B visas were given to Indian nationals.

Automation 

It is a known fact that Automation has gathered pace over the last couple of years.
Low skill mundane tasks can now be automated and replaced, due to advances such as Artificial Intelligence, Process Automation, and Cloud Computing. Since jobs especially in India, in certain domains, were large of modest skills, the impact is graver. There is an immediate need to change and upscale essentially for people from IT and Engineering backgrounds, majorly in the mid or entry level jobs.
There is a whole diverse nature of jobs and parallel growth opportunities that have come up in this situation. While mundane skills are outdated, there are certain other requirements with major skill gap issues. It is the right time to dwell into and get yourself upskilled.

Leverage the Impact – Upskill Now! 

Consider the Analytics domain, which according to NASSCOM, the sector is witnessing exponential growth and is expected to be worth $16 billion by 2025.

Imarticus Learning’s awarding winning Data Science Prodegree, in collaboration with Genpact as a Knowledge Partner, offers a comprehensive curriculum and is ideal for IT professionals to leverage their current skill sets and transition to the Data Analytics space.

Imarticus also offers certifications in Business Analytics, Big Data Hadoop, SAS Certification, R Programming, Python, Data Visualization courses, which are strategically designed in association with industry stalwarts.
Taking up these courses with Imarticus will help your career stir out of the testing times ahead, leveraging the impact of the downfall in the IT services industry.
Survival of the fittest is an essential law of nature. IT services are undergoing a revolution and to keep up with the pace, one will have to adapt and embrace the new.

Data Science is essentially a process of a lot of iteration. To complete a project in data science, one will have to make many changes, consistently during the process while trying new ideas.
The first step first, lets us make it clear, especially for the ones who would like to pursue their career in data science, to not confuse a job of a data scientist to that of a software engineer. Methodologies of software engineering cannot be used in data science. Data science is more of science and less of engineering. There are some relevant software’s in data science that assist in optimising workflow, however, it is also the clarity, experience and intuition of the data scientist and the team that sets the preliminary analysis on track.
If a data science project has taken longer than planned to complete, it could be because of iterations. Let us understand, iterations will happen during the course of a project, however, if an iteration is for any other reason besides the flow of new information, it is uncalled for and could have been eliminated.
An unprecedented iteration could be either because the business pain point was not identified correctly, the data scientist was not aligned with the company objective, a data scientist did not initially believe in a collection of a few variables, or it could be because of assumptions and biases in the data were not accounted for. These are just a few scenarios which can be easily avoided.
Imagine if all variables are not accounted for, one will have to do the analysis again, and that would be really time-consuming, also counterproductive for the project and the team working on it.
Some tips to avoid such scenarios:

1. Identify and choose the right issue to use the skills of a data scientist and the advantages of applying data analytics. Do not try to solve every resolvable issue with this technique. Apply data science only if the concern or problem is large enough, and clearly identify as to what objective or hypothesis you are running with. Check for the alignment of that hypothesis with the desired business outcome. Break down a large issue with all possible outcomes and then ask at each step what variables would be required. Defining each factor, and applicability of outcomes would be a great starting point. Make use of pipeline and data sharing tools.

2. Identify the data requirement, this is simple, define the time period you would need the data from, collect all information and data points even if it might not look important now, and lastly put a structure to your data requirement by designing tables, this will also further add clarity to what variables would be captured.

3. This step is simple yet mostly faltered on, always ensure that the analysis created is reproducible.

4. It’s a daunting task to write codes, now imagine to continue writing it over and over again. To avoid syntax errors, it would be great to make a directory of most commonly used codes and ensure everyone on the team has this, it will ensure efficiency in work and it also takes care of simple errors.

5. Be flexible and adaptive to technologies, there is no process that is perfect. Be adaptive to the limitations in technology and processes, always finding an alternative will help you reach the goal faster.

6. Understand the business, you might be a pro in programming and numbers, and data analysis comes naturally to you, however, if you fail to understand how your business works there will always be a gap in understanding the output.

7. Speak the language of your stakeholders, they might not understand algorithms and you should not assume they understand the technical language. Help them visualise your findings, your approaches. Use visuals and examples to illustrate your plan. Connecting with the audience is half the battle won.

Demarcate the data science project in four phases –
The first phase is the Preliminary Analysis -this is where an overview of data points is done.
Second Phase is the Exploratory Data Phase – Specific to asking the right questions and cleaning the data to answer those questions.
Third Phase is Data Visualisation – Here the focus shifts on how to present the analysis.
The Fourth Phase is Knowledge Discovery Phase – The last stage, where models are made to explain the data, algorithms are tested to come up with the best outcome possible.
This is not a definitive workflow and one could make changes to further increase efficiency and productivity. Data Science is exploratory in nature where the data scientist is constantly innovating and learning, preparing themselves to overcome business and project challenges.

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Read More:
Having Technical Knowledge Is Not Enough For Data Scientists
What a Data Scientist Could Do…?
Seven ways a Data Scientist Can Add Value to Businesses

Less is more, organisations are undergoing some major restructuring based on this philosophy. IT, in particular, is the department under the scanner, majorly across the year 2017. There is a big shift in the way this department is being viewed by the management. They now want to look at it as a revenue generator and not a cost factor.

The dawn of 2017 has seen some historic lows in the unemployment rates for the IT industry. Having said that, it does not mean the life of an IT professional is a short one. As always the case with evolution, the old gives way to the new, while some roles might not have the same value in the field of IT that they had a couple of years ago, there are some new roles which are trending like hot cakes. Hence an IT professional, in current times will have to wear many hats to exist.

In today’s scenario, an IT professional is challenged to add value to the organisation. So how do they do that? Simple, by upscaling few skills in the right domain, which is the need of the hour or might be needed in the near future. So in simple words, what is really needed is a reshuffling of skills rather than looking out for alternative careers.

Let’s understand the roles that individuals will need to adapt in order to be desirable talent.

Efficiency in Programming

Organisations have started focusing on minimising costs and minimising time so that they can focus more on understanding and solving business challenges. Hence the IT associated with the organisation cannot have a one-dimensional approach as a software developer but will need to be nothing short of an all-rounder, by showing efficiency in multiple languages of programming, i.e., DevOps, QA, DBA, Analytics and additional tools.

Cybersecurity

Now in this particular area, the demand clearly exceeds the supply. Security Auditor Information, Security Manager, Cyber Security Architect are some of the roles that need appropriate talent. Recent headlines about some high-profile breaches have alerted big corporations on methods of securing their businesses before they are compromised.

Networking Professionals

According to some research, the networking professionals will continue to be in demand in the coming years in spite of a stall in jobs some time ago. Especially Network Administration and Network Engineers are some of the skills that are in demand. It is predicted that the digital business will also flourish and one will see growth there too.

Designers and Developers and QA Testing

With the ability to mimic human emotion and intuition, using techniques of Machine Learning and Artificial Intelligence, together will give rise to client expectations in providing effortless and easy navigation on e-commerce sites and gaming sites. User Experience (UX) or User Interface(UI) Designers will be sought after and high in demand.
Hand in hand goes the Quality Assurance Testing at the back end of this interface development, to ensure that not only the customer-facing end is seamless, but it also has the strong backing of robust software behind it.

Cloud Engineers

There is a phenomenal growth in the evolution of cloud computing skills required by organisations, Cloud/Azure Architect, to AWS Consultant or Cloud Engineer and Developers are some of the roles which are in high demand. There is a shift towards Public Cloud Infrastructure and Hybrid Cloud Adoption solutions. The organisations and big corporates have already built strategies towards their needs in cloud solutions and in recent times are building teams for implementation.

Big Data

Data Scientist, Data Engineer, Data Architect, are some roles that most companies are outlining and intent to build on, as they continuously struggle with the flow of unstructured data from various sources. Organisations have understood the value of this data, and look at trained professionals to steer the organisational strategies in the right direction, using the information from this data.

Professionals who can develop solutions to Capture, Analyse, Interpret and Explain the findings will continue to be pivotal for organisations across industries. Hadoop, R, Python, SAS and knowledge of other programming languages should be acquired to show flexibility and efficiency on the job.

There is a radical change in the way the world of technology works, things which use to take 300 lines to programme, with changing technology can be done in less than 15 lines. The IT jobs that will be in demand in the near future are particularly from fields of Cloud Computing, Big Data and Analytics. The workplace and the workforce need to adapt to this change. Up-skilling thus becomes the need of the hour.

Big data is nothing but a large pool flooded with random information. Without the required expertise in churning this data into actionable insights, big data is nothing but just random information which has the potential of powerful insight, which is just lying dormant.
Lately, a lot of organizations from across sectors are recognizing this and opening their doors to big data and unleashing its power. Thus, these are shining times which increase the value of a data scientist who not only understands the value of this already existing information but also has the skills to derive valuable insights from the data.
It is getting clearer by the day, that the value lies not in the data but on how the data is analyzed and processed and that is the where a data scientist steps into the limelight. Organization have started to understand the value of data science. However, most are still unaware the value that a data scientist holds in a company and the areas that they can impact.

When do Companies Benefit from a Data Scientist?

Any data scientist usually holds an advanced degree either in statistics, math or computer science. It becomes imperative for them to have an academic knowledge of the same. And along with this they need to have good domain knowledge, where they can handle skills such as information management, visualization, data mining, data analytics, knowledge in programming tools like, SAS, R programming language, Python etc…, it is also common for them to have knowledge in machine learning, infrastructure design, cloud computing or data warehousing.
A company generally benefits from the skills of a data scientist, when they have a need to crunch volumes of numbers, when they have or wish to possess ongoing operational customer information, or generally when they see the benefit from social media forums, market research or consumer research, credit data, or information acquired from third party data sets.
Seven methods or ways in which a Data scientist can Add Value to Any Business…..

Partner with Management to make Informed Decisions

A major role of a data scientist is to become a trusted advisor to the management. By ensuring the entity that they are associated with, maximises its analytical capabilities. A data scientist will be responsible for measuring and tracking all performance metrics across all levels in an organisation, thus assisting the entity by facilitating an improved process of decision making through data analytics.

Defining Actionable Goals

A data scientist can steer the direction in which the company should move, based on the data received. They can recommend certain trends based on which actions can be adapted which will help in customer acquisition, retention, engagement and thus, as a result, improve profitability. In doing so the data scientist aligns the organization across levels in understanding the benefits of data analytics and further trains them in the effective use of the system to extract insight and derive action. An aligned staff can then better pay attention in addressing key business challenges.

Identify Opportunities by staying Inquisitive

They have to question, analyze the current analytics system on a continuous basis. As only then will they be able to develop additional algorithms. As in Kaizen, their job is to add continuous improvement in the value that is derived from the organization’s data.

Data Driven Decision Making

They have to assist the organization to take decisions with the help of facts, they add value by ensuring that the decisions are low risk, with a minimal margin of error. A data scientist helps with facts instead of intuition.

Testing Hypothesis or Results

Setting the path with the help of actionable insights is only half the job done, after all, they are insights, low-risk decisions based on factual findings. The other half is to ensure that the organization is steering in the right direction if those insights are actually true. It is essential to assess how those decisions have impacted the organization. A data scientist is the one who measures the key performance metrics and quantifies the success.

Target Audience

Key groups can be identified with precision by analyzing the source from the data, with this in-depth knowledge organizations can segment their products or services by demographics and increase cost and profitability.

Talent Acquisition

Recruitment is a daunting task, it generally requires scanning of multiple portals and CV’s. with the right algorithms, data scientist can help in the process of scanning in house or external portals and can further assist in data driven aptitude test screening, thus cutting down the cost in hiring.
Data science can definitely add value to any business in recent times. The above mentioned are only a few most commonly mentioned practices. Whether it is talent acquisition or strategic alliance with the leadership to make informed decision, it is true data science is here to help businesses function with profitability.

In simple words and or in a sentence it can be defined that a data scientists job is to analyse data and give valuable and actionable insights.
Of course if it would be this simple then Harvard would not call it ‘The sexiest job of the 21^st century’ so clearly the data scientist job comes with some prerequisites, certain qualification and skills along with experience. The role cannot be easily defined, there is no standard set of skills and experience which can be defined, and even if we go to an extent and make an exhaustive list, the skill sets will be nearly impossible to find in one person.  There are many nomenclatures that are attached to the wider role, like data engineers, data analyst, data mining specialist etc.., which further confuses organisations and even individuals who wish to pursue a career in data science.
So a data scientist could draw upon a few common disciplines, it is important to note that a data scientists level of experience and knowledge in each domain varies in degrees, starting from beginner, to proficient and expert.
Some common disciplines include, knowledge in math, statistics, advanced computing knowledge, visualisation techniques, to have a hacker’s mind set would be an advantage, essential and relevant domain expertise, and lastly a data scientist has to be excellent in data engineering and also be skilled in scientific methods of data processing.
You can become a data scientist with mostly the common educational and work experience background. However, your effectiveness and efficiency can be determined by how good you are in the four fundamental areas.

• Business Domain
• Statistics and probability
• Computer science and software programming
• Written and verbal communication

Consider these as the four pillars for a good data scientist. Based on these four pillars, a data scientist will be able to leverage existing data sources and create new ones in order to extract meaningful information and actionable insights. With the help of these insights businesses can take decisions intended to increase revenue.

Data Scientist do…

• The identification of data analytics problem which in turn offers great opportunities to the business
• The determination of the correct data sets and variables to look into
• Collection of structured and unstructured data sets from all possible sources
• Separation of unstructured data, validating it to ensure accuracy and completeness
• Using algorithms to mine targeted data
• Analysing the information and identify patterns and trends with an inquisitive mind Exploratory data analysis
• Interpreting the data to see factual finding, creating hypothesis and trends, proving it with findings
• Communicating in clear language with the leadership team so that everyone can understand the implications easily.

Harvard is right when it says that data scientist is a highly important and demanding role that can have a huge impact on business, in many ways, financial, operational, strategic and so on.
Companies collect a lot of data in recent times, this data is usually neglected or underutilized. There is a lot of insight one could get from this data if it is extracted for actionable information it can optimize customer success, and subsequent acquisition, retention and growth. A data scientist is the person who can make this possible.

R or SAS – What is Beneficial for a Data Scientist?

Data Scientist or an Analytics profession is the calling of recent times. This is a new breed of professionals who possess the technical skills required to solve the complex problem and also are inquisitive enough to come up with problems that need a solution. This, in turn, assists corporate to come up with predictive analysis to spot trends and help come up with realistic solutions. Data scientist or analyst work with high volumes of data to drive to conclusions. They are part mathematicians, part computer scientist, and trendsetters. Huge volumes of unstructured data or Big Data cannot be ignored but is considered as a gold mine that helps increase the revenue of organisation across different fields like, financial, IT, retail, Hospitality, education, in short, the whole spectrum.
Earlier data scientist started their careers as either statisticians or data analysts. But with the evolution of big data, there has been a growth in their roles too. Data is no longer just associated with IT, it requires systematic analyses a creative curiosity and most importantly, knowledge of tools to translate great ideas and information into a simple presentation for the non-technical audience responsible for taking the decisions.
And thus, for an individual who is in the process of advancing his career in data science, comes the struggle to choose the right tool for the job. It is an ongoing battle as to which programming language is best suited for data analysis. And although in recent times there are many options that are available, the traditional question is primarily always between SAS or R, with Python as the new entrant which cannot be ignored.

Main difference between both programming tools –
Open v/s Closed – SAS is a closed source; it requires licences and approvals, hence it does not support transparent functionalities. R programming language and even Python coding program are open sources and as opposed to SAS contains detailed transparency of all of its functionality.
Cost – SAS is one of the most expensive tools to existing. Since R is an open source software, it can be downloaded for free by anyone.
Learning – SAS is fairly easy to learn, especially if one has the basic SQL knowledge, it has a stable GUI interface. Tutorials of SAS are also available on various sites. R is a low-level programming language and hence it requires complex codes for shorter procedures, one needs deeper insights of coding in R.
Accessibility – Almost all advanced features need licenses for their new products on SAS, increasing the cost and accessibility. Whereas R allows to access or upgrade to the advanced features easily.
Graphical Capabilities – SAS has basic graphical capabilities, but it is only functional. With reference to this factor, R has the best graphical capabilities when compared with the SAS.
Let us further understand the Description of the Tools –
SAS – SAS is considered to be the leader in the data analytics field, it is an integrated software solution. This software also has a lot of good features like GUI and excellent technical support. It is generally used to perform tasks such as data entry, retrieval and management, for report writing, conducting statistical and mathematical analyses, for research in operations and project management. SAS is one of the oldest and most trusted programming tools used by big global corporate, especially in the field of finance. Some reputed companies that use SAS are Barclays, HSBC, PNB Paribas, and Nestle etc…,
R – R is a programming tool for statistical computing and graphics, it offers a wide range of techniques. Since it is an open source tool, it is highly extensible. It is a simple and effective programming language and it is more than just a statistic system. It is generally used to perform tasks such as visualising data, Machine Learning etc…, R is also used by reputed companies but is usually popular with startups and mid-sized organisations.
So to conclude if one has a goal to become a business analyst professional and is planning to join a bank or the financial services where the company is using SAS and might want to fund the course or will partially fund the learning, then you should take up SAS and maybe later learn R once comfortable with SAS. Remember learning SAS programming course might be fairly easy, but is very expensive and if one wants to join a start-up where SAS is not used, then to have the skill is of little use. In such a scenario it is better to learn R, also it is advisable to learn R if you have a statistic or a programming background.
Having knowledge in R and SAS is imperative if you want to excel in the profession of Data Science.