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Improve Your Business Intelligence Strategy In Just Six Steps!

When Moore’s Law meets with modern day Business Intelligence, what happens? Disruption and then wider adoption!

Improve Your Business Intelligence Strategy In Just Six Steps!

With costs of implementing BI tools lowering, more and more enterprises are keen on jumping on-board the homebrewed variety of custom BI solution to help drive their business. The result of these efforts is that these days several organizations are pursuing data driven intelligent decision-making, at a cost, which is almost fractional compared to yesteryear’s Business Intelligence budgets.

A proper Big Data certification allows individuals to make the best of available smart BI solutions available out there!

But the question remains, as to are all these companies actually making better decisions?

Surely, most enterprises are now reaping the benefits of having a larger range of BI solutions available to them. Nevertheless, there is still a bigger room for error in the picture, which many firms tend to ignore.

If done right, BI solutions can deliver an ROI of USD 10.66 for the cost of every dollar spent on implementing them. But, as per a survey conducted by Gartner, the results are not so glorious for most firms. More than 70 percent of all BI implementations do not stand up to meet the business goals that were anticipated of them.

Due to the evolution and lowering BI solution prices, the demand for data analytics certification courses have grown by several manifolds.

Is there a secret formula to BI solution driven success? Well, starting with asking the right questions is always a good place to begin:

Here are six steps that can tip the balance in your favour:

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 Which data sources to use?

Do you know what the lifeblood is for BI? Why, data of course, data is what Business Intelligence strives upon. All firms do have a rudimentary strategy to collect and analyze data, however, they tend to overlook the data sources. The key here to note is – truly reliable data sources are the main difference between the success and failure of your Business Intelligence efforts.

These data sources do exist; all you have to do is choose right. In addition, the best thing about them is a lot of them are almost free of charge. Using the good ones will transform the way you look at your market, the business pipeline and the way you perceive your audience.

Are you warehousing your precious data right?

These are your firm’s single source data repositories. Warehouses store all the data you collect from various sources, and provide the same for when needed, on prompt for reporting and analysis. However, self-service BI tools can be a bit of hit-or-miss at times, where consistently handling data is a worry.

The key is to discover a data warehouse solution, which can efficiently store, curate and retrieve data for analysis on prompt.

Are your analytics solutions good enough?

Companies that are looking to use their own Business Intelligence infrastructures must identify the analytics architecture that best suits their necessities. However, unwieldy datasets in combination with a lack of processing maturity can dull the effort even before one decides to start!

How does your BI solution integrate with the existing platforms?

For incorporating enterprise-scale Business Intelligence solutions, it is necessary to have it work effortlessly with the different other information formats, processes and systems, which have already been established previously in the internal work pipeline.

So, the key here is to ask the question – will the necessary integration cost more in terms of resources and effort that you can afford?

Use reporting mechanisms that are both powerful as well as easy to understand:

The most persistent challenge in BI is to wrangle data, majority of users cannot understand any of it beyond a simplified visualization. Decision-makers may be fooled with the help of powerful visualization tools. However, the truth is that making it pretty alone will not get the job done right.

So, forget pretty, and ask the all important question of whether the reporting mechanism is useful in interpreting otherwise unintelligible data or not.

Has better compliance enabled through your Bi solutions?

If your BI solutions, directly impinges on relevant regulations (and so it will, when the time comes). Then the solutions should aid the compliance and not hinder it. A good BI solution should provide a means to trace and audit data and its sources wherever, needed.

In conclusion: the success of your efforts will ultimately depend on the data.

The field of data science is evolving in expertise. And even professionals involved in the field tend to vary in their capabilities and opinions about the same. So, the important thing is to consider the importance of data in your company, and that one has all the appropriate responses to the posed questions above.

You can learn to ask the right questions with comprehensive tableau BI training courses. For more information on tableau course details feel free to contact the experts at DexLab Analytics.

 

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Five Major Big Data Trends That Will Shape AI this New Year

Many still believe that Big Data is a grossly misunderstood, mega trending buzzword in the tech field even today. However, there is still no denying of the fact that the recent development of AI and machine learning push is related on the synthesis and labelling of huge amounts of training data. A latest trend report by the advisory firm Ovum predicted that the Big Data market which currently is valued to be USD 1.7 billion, will further rise to be USD 9.4 billion by 2020.

 

Five Major Big Data Trends That Will Shape AI This New Year

 

Then what do the insiders in the data analytics market see it happening in the upcoming year ahead? We at DexLab Analytics, the premiere Big Data Hadoop institute in Delhi spoke to several leaders in this field to discover.

 

Here is what we found to be the five most important trends that will shape the future of machine learning, AI and data analytics in 2017 from the industry experts:

 

The predictions strongly emphasize the need for more talent and skilled personnel in this vast field of data analytics, thus, a growing demand for Big Data training and Big Data courses will be witnessed worldwide.

Continue reading “Five Major Big Data Trends That Will Shape AI this New Year”

Governance is Planning To Drive Compliance With GDPR

Governance is Planning To Drive Compliance With GDPR

The companies’ data governance program is usually linked to the implementation of the General Data Regulation Program. And throughout time several articles have been written about to link the two initiatives but till none was so clearly distinct as the recent one on LinkedIn by Dennis Slattery. He made an analogy of a wedding between Governance and Privacy, which is very fitting but also highlights the fact that, a long term marriage with optimum success is based on the foundations that strengthen it with mutual efforts.

We can also take a similar message from the famous quote by Henry Ford – coming together is just the beginning, keeping together is actual progress, and working together is success.

Data analytics should tie its hands with privacy policies for a successful approach towards good business.

So, how can we make this marriage successful?

The GDPR regulation is quite clear on what it states, about things that must be done in order to protect the Data Citizen’s Rights. However, the bigger question most companies are facing is how to comply with regulations and/or go beyond the bare minimum and let GDPR work for them.

Majority of such discussions around the topic of how to implement GDPR today are focussed on one of two approaches – either top down or bottoms up. But we would argue otherwise, as these two approaches are not mutually exclusive and that a successful implementation of the GDPR must be based on a combination of these complementary approaches.

For the top down approach, the team for GDPR will reach out to the businesses to get a clear understanding of all business (data) processes, which involve either one or another. And for each of these processes like for third party credit checks, data analytics, address verification, and much more there are several attributes which must be clarified. Like for instance:

  1. Have they acquired consent for the particular process?
  2. What is the business purpose for the collection?
  3. Who is the controller?
  4. Who is the processor?
  5. Who is responsible as the Data protection officer?
  6. What is the period for retention of data?
  7. What type of data is collected?
  8. Along with several other information

However, it must be noted that this is not a one-time effort, once all the processes related to the personal data have been identified and classified they will still be needed to be maintained as the organization grows and evolves with development in its infrastructure over time.

The bottom up approach is a little more technical in nature. The businesses that have already established metadata management tools can then use these technologies to identify personally the identifiable information (PII) and then try and classify these data elements and assign the relevant attributes for GDPR. This approach shall quickly hit a bottleneck as the same data can be utilized for several business purposes and thus, cannot be classified for GDPR.

With successful implementation of the GDPR we will be able to marry both the approaches well.

Big Data Hadoop training from DexLab Analytics from Dexlab Analytics

 

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How To Stop Big Data Projects From Failing?

Here in this post we will discuss with inspiration from the views of insider experts about how Big Data teams and IT personnel can make sense from the right kinds of data which will ultimately allow executives to make smarter business choices and drive results for their business.

 

Big data hadoop certification in pune

 
The amount of data that has been created in the last two years is much more than the amount that has been created in the entire previous history of our human kind. This has led to an explosion of data analyst training institutes popping up every now and then and welcoming students from diverse backgrounds. Continue reading “How To Stop Big Data Projects From Failing?”

Why Getting a Big Data Certification Will Benefit Your Small Business

Do you know how much data is currently produced globally every year?

 

As per the reports published by IBM, the figures are 2.5 QB (Quintillion Bytes). The numeric representation of the same looks as: 2,500,000,000,000,000,000. And we thought that our mobile devices with 64GB memory space are capable of storing huge data.

 

Why Getting a Big Data Certification Will Benefit Your Small Business

Increasing reliance on Big Data

As technology is expanding at the speed next to light, more companies are planning to invest in Big Data platforms for getting the best out of it. Gartner Inc. had conducted a research recently among 437 global organisations across different industries and figured out that more than 75% of them are looking forward to the benefits they can derive from Big Data. The purpose for using Big Data varied to some instance across these organisations, however most of the companies were found to use data analytics for enhancing their customer service segments. Recently, security breach has hit the headline more often than global warming and that has been a factor of worry for many data driven companies. Thus, they are opting for Big Data tools in order to strengthen their online security. Continue reading “Why Getting a Big Data Certification Will Benefit Your Small Business”

Big Data is the New Obsession of Small Business Owners

Big Data is the New Obsession of Small Business Owners

While this may seem somewhat counterintuitive, but instead of large organizations, it is actually small business owners and midsize companies who tend to be more inclined towards the applications of Big Data. They are also the first to adopt these latest technological innovations of which analytics is no exception – as these internet and data based insights are highly accessible and also affordable for SMBs.

As per the researchers in the fields of technology, the entry level capabilities in such fields like analytics has abruptly dropped which is why almost all types of industries from an array of sectors are engaging with them to enhance their competitiveness; and the wheels have already started to roll when it comes to increasing overall global competitiveness. Continue reading “Big Data is the New Obsession of Small Business Owners”

How Amazon Uses Big Data for Success

How Amazon uses Big Data for success

Taking a stroll around the lanes of Big Data is no cake walk. The main problem being that well, Big Data is big to tackle and on top of that complex to analyze and draw insights from. That is why the world needs more data analysts. Also the many nuances of Big Data architecture make it especially difficult for the concerned personnel to grasp its requirements. Also the concept is relatively new there is a lack of understanding and experience in the field of Big Data which is often the management of major corporations misuse their Big Data.

The best way to learn about how you can use your company’s Big Data effectively is by paying a close attention to how other companies have used their data and by effectively implementing similar practices. One such company who has done so is Amazon.com.

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There is no hint of doubt about the data expertise of Amazon.com as it is one of the key innovators in the realm of Big Data technology. This is a giant that has given us a great idea on how to collect, analyze and then successfully implement data analytical reports. Moreover, in addition to using Big Data successfully for its own purpose the company has also leveraged its own data usage tools for helping others with tools like Amazon Elastic MapReduce.

Amazon has taught us several lessons on how to successfully implement Big Data to amplify revenue generation:

Get your eyes on the customer:

The premier uses of Amazon’s Big Data are with its customer recommendations. If one has an Amazon account they use on a regular basis then you will notice that all the recommendations on your homepage are based on your browsing history. Everything including sale items to special discount offers is based on your previous purchases and your product browsing history. Now you may argue that even several other sites including the whole of internet works like that, but while they might a frequent occurrence today Amazon was among the first ones to start this trend.

It was one of the first organizations to provide its customers with a focused and personalized buying recommendation that made them buy more. Who knew the best way to make people buy more than they want was just to tell them that with an enticing deal?! This solution is a simple one and works for several problems.

This is the best lesson that Amazon has taught the business world. For any business to succeed and to use Big Data well the main focus should be on the customers. If your customers are happy then you will be better off at your business. That is the basic rule of thumb when it comes to business after all.

Sniff out all the data you can:

This retailing giant uses Big Data gathering tools and uses it to the best of its advantages. The company gathers a lot of data by the hour or better put by the second. So, it might be easy to lose focus on why data is being gathered and which type is necessary or how it can be useful to the customers. But this company does not let those parts slide. The company gathers and analyzes its data diligently and never fails to upgrade its workings with the findings.

Big Data has worked for Amazon now make sure it works for you take Big Data courses to better handle your data.

 

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Big Data Hacks: 5 Amazing Free Data Sources

Big data hacks: 5 amazing free data sources

With the data explosion revealing a continuum of numbers and facts and figures across the web and across businesses, it is of no doubt that data is omnipresent. But as the saying goes, sometimes it is hard to see the forest due to all the trees.  A big myth among several companies is that they need to hire data analysts to look for their own data for analysis and to reap the benefits from Big Data analytics. But you must realize that this is far from the truth.

There are more than hundreds in fact even thousands of free data sets available for analysis and use for those who are smart enough to know where to look for them. Here is a list of 5 most popular free data set sources that are widely used globally. There are several more out there for those who are keen enough to look for them.

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  • Data.gov: in compliance to the promise made by the US government last year all the government data is available for free on the internet in this site. The site is a useful source of information on everything starting from numbers in association to crime to climate change and much more.
  • Socrata: another great place to get scoop on the latest government related data along with some useful visualization tools that come built into the web portal.
  • org another place to access government data for free. One can get access to government data from the US, Canada, EU, CKAN and more.
  • World Health Organization data portal: a place to access all the statistics of hunger, health and disease of the world can be accessed here.
  • FaceBook Graph: FaceBook over the past few years has tightened their security and privacy settings. But there are still some amounts of data open to eyes without any privacy. And FaceBook provides information and access to all this data with their Graph API. While users may not be happy to share them with the world, they probably have not yet figured out how to hide them.

A bonus free data source that could also be fun to explore.

Face.com: get face recognition data with this fascinating tool and analyze possibilities like the creator.

These days a lot of forward thinking companies are trying to data driven, but they may not have ample resources to get their own data right away. So, it may be a good idea to begin with these publicly available free data sources. The best tip for data scientists is to learn to ask the right questions to get the right answers.

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A few easy steps to be a SUCCESSFUL Data Scientist

A-few-easy-steps-to-be-a-successful-data-scientist (1)

Data science has soared high for the past few years now; sending the job market into turbo pace where organizations are opening up their C-suite positions for unicorns to take their mountainous heap of data and make sense of it all to generate the big bucks. And professionals from a variety of fields are now eyeing the attractive position of data analyst as a possible profitable career move.

We went about questioning the faculty at our premiere data science and excel dashboard training institute to know how one can emerge as a successful data scientist, in this fast expanding field. We wanted to take an objective position from a recruiter’s point of view and create a list of technical and non-technical skills which are essential to be deemed an asset employee in the field of data science.

Keep Pace with Automation: Emerging Data Science Jobs in India – @Dexlabanalytics.

A noteworthy point to be mentioned here is that every other organization will evaluate skills and knowledge in different tools with varying perspectives. Thus, this list in no way is an exhaustive one. But if a candidate has these songs then he/she will make a strong case in their favor as a potential data scientist.

The technical aspects:

Academia:

Most data scientists are highly educated professionals with more than 88 percent of them having a Master’s degree and 46 percent of them have a PhD degree. There are exceptions to these generalized figures but a strong educational background is necessary for aspiring data scientists to understand the complex subject of data science in depth. The field of data science can be seen in the middle of a Venn diagram with intersecting circles of subjects like Mathematics and Statistics 32%, Engineering 16% and Computer Science and Programming 19%.

Knowledge in applications like SAS and/or R Programming:

In depth knowledge in any one of the above tools is absolutely necessary for aspiring data scientists as these form the foundation of data analysis and predictive modeling. Different companies give preference to different analysis tools from R and SAS, a relatively new open source program that is also slowly being incorporated into companies is Hadoop.

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For those from a computer science background:

  • Coding skills in Python – the most common coding language currently in use in Python. But some companies may also demand their data scientists to know Perl, C++, Java or C.
  • Understanding of Hadoop environment – not always an absolute necessity but can prove to be advantageous in most cases. Another strong selling point may be experience in Pig or Hive. Acquaintance with cloud based tools like Amazon S3 may also be advantageous.
  • Must have the ability to work with unstructured data with knowledge in NoSQL and must be proficient in executing complex queries in SQL.

Non-technical skills:

  • Impeccable communicational skills so that data personnel can translate their technical findings into non-technical inputs comprehensible by the non-techies like sales and marketing.
  • A strong understanding of the business or the industry the company operates in. leverage the company’s data to achieve its business objectives with strong business acumen.
  • Must have profound intellectual curiosity to filter out the problem areas and find solutions against the same.

 

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