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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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What is Truly Efficient? Understanding Stratified Random Sample

What is Truly Efficient?  Understanding Stratified Random Sample:

We have discussed several times the efficiency of various techniques for selecting a simple random sample from an expansive dataset. With PROC SURVEYSELECT will do the job easily…

 

proc surveyselect data=large out=sample
	 method=srs   /* simple random sample */
	 rate=.01;   /* 1% sample rate       */
run;

 

However, let us assume that our data includes a STATE variable, and one would want to guarantee that a random sample includes the precise proportion of observations from each of the states of America.

Continue reading “What is Truly Efficient? Understanding Stratified Random Sample”

How to Assess Clustering Tendency: Unsupervised Machine Learning

How To Assess Clustering Tendency: Unsupervised Machine Learning

The meaning of clustering algorithms include partitioning methods (PAM, K-means, FANNY, CLARA etc) along with hierarchical clustering which are used to split the dataset into two groups or clusters of similar objects.

A natural question that comes, before applying any clustering method on the dataset is:

Does the dataset comprise of any inherent clusters?

A big problem associated to this, in case of unsupervised machine learning is that clustering methods often return clusters even though the data does not include any clusters. Put in other words, if one blindly applies a clustering analysis on a dataset, it will divide the data into several clusters because that is precisely what they are supposed to do. Continue reading “How to Assess Clustering Tendency: Unsupervised Machine Learning”

Here Are Four Predictions For AI This 2017!

Last year was the year, which saw artificial intelligence, went mainstream.

 

Here Are Four Predictions For AI This 2017!

 

By that, we do not mean just getting filtered raunchy photos on Twitter or getting the fake news suggestions on Facebook.

Here is what to look for in Artificial Intelligence for this New Year:

  • Driven by unprecedented financial support (along with a growing open source ecosystem), founders have been delivering artificial intelligence start-ups at a record high rate.
  • GE, Google, Intel, Microsoft, Facebook, Apple, Salesforce and Samsung, and several other name brands made rigorous AI investments last year.
  • There are now five million homes, which, are talking about their music and shopping choices with the help of Alexa from Amazon.
  • There is a whole new department of U.S. Department of Transportation Committee for self-driving cars. Even a few years ago, there were people talking about 2025 or so for the accessibility of self-driving cars (of level 5 autonomy), but this is a reality now, much before we could reach 2020. It is also amazing to think that self-driving cars may whittle down the 1.2 million annual deaths from automobiles.
  • Also in other interesting news, two AI unicorns just grew their horns, the Cylance in Silicon Valley and iCarbonX in China.
  • Also more than one-fifth of the MIT 50 smartest companies list, include AI as a core approach these days.

Continue reading “Here Are Four Predictions For AI This 2017!”

The Olympics Turn To Data Analysis: Canadian Olympic Committee Deals In With Analytics

The Canadian Olympic company has recently teamed up with a major Big Data Company to ramp up the analytics for the benefit of the athletes.

 
The Olympics Turn To Data Analysis: Canadian Olympic Committee Deals In With Analytics
 

Recently the COC made an announcement about an eight-year, cash and services sponsorship deal with SAS, which is an analytics software with a brag-worthy client list from varied industries, like universities, hotels, banks casinos and much more.

Continue reading “The Olympics Turn To Data Analysis: Canadian Olympic Committee Deals In With Analytics”

How to Use PUT and %PUT Statements in SAS: 6 Tips

The PUT statement in SAS for programmers who have completed a SAS certification in the DATA step and the %PUT macro statements are highly useful statements, which will help to enable you to display the values of variables and macro variables, respectively.

 

How to Use PUT and %PUT Statements in SAS: 6 Tips

 

And almost by default the output will appear in the SAS logs. In this article we will share a few tips which will allow you to make use of these statements more efficiently.

Continue reading “How to Use PUT and %PUT Statements in SAS: 6 Tips”

The Choice Between SAS Vs. R Vs. Python: Which to Learn First?

It is a well-known fact that Python, R and SAS are the most important three languages to be learnt for data analysis.

 

The Choice Between SAS Vs. R Vs. Python: Which to Learn First?

 

If you are a fresh blood in the data science community and are not experienced in any of the above-mentioned languages, then it makes a lot of sense to be acquainted with R, SAS or Python.

Continue reading “The Choice Between SAS Vs. R Vs. Python: Which to Learn First?”

You Must Put These Data Analytics Books in Your Reading List This Year

To be a successful data analyst, you must share two very important attributes that you must possess:

 

  1. You must be a voracious reader in order to keep up with the developments in the industry
  2. You must be willing to share your knowledge with the people in a simplified manner, so that everyone around you also gets access to this knowledge
     
    You Must Put These Data Analytics Books in Your Reading List This Year

 

That is because the universe around us deals in the common currency of information and wisdom, which should flow freely without any price tags on it.

Continue reading “You Must Put These Data Analytics Books in Your Reading List This Year”

5 Major Problems in AI (Artificial Intelligence)

5 Major Problems in AI (Artificial Intelligence)

Before we get started with the topic, let us first get an idea about its background. Have you ever given a thought as to how many cats does it take to identify one cat?

In this article we will cover the five types of problems that people face with Artificial Intelligence (AI) i.e. we will address the all important question of – in which situation must one make use of AI (artificial intelligence)?

To have a better understanding of such concepts you can take up a Machine Learning course in Delhi.

Here is some background:

Just some time ago, we conducted a strategy workshop for a bunch of senior executives who are running a large multinational company. In that workshop, someone asked this question – “How many cats will it need to identify a cat?”

In this post, we will discuss the problems which can be uniquely resolved through Artificial Intelligence. While this may not be the exact taxonomy, but it still is pretty comprehensive. The main reason we have added extra emphasis on Enterprise AI issues, because we believe that this subject will have a deep impact on many mainstream applications, but despite that a lot of media attention focuses at the more esoteric avenues. Further, information about these concepts are available in our Machine Learning training course.

But before we delve into AI application types, we must discuss the main distinguishing characters between AI / Deep Learning / Machine Learning.

The term Artificial Intelligence by definition implies that machines can reason with the help of this feature. However, here is a better more complete list of AI characteristics:

  1. AI is capable of reasoning: they can solve complex problems through logical deductions on their own
  2. AI has knowledge: the capability to represent knowledge about the world or our understanding of it, that there are numerous events, entities, and varied situations that occur in the world and such elements have properties, which can be categorised.
  3. AI can plan: they have the ability to set and achieve targets. A specific state of the planet, which we desire along with a sequence of actions that can be undertaken which will help us, progress towards it.
  4. AI can communicate: they have the capability to comprehend well-written and spoken language.
  5. AI has its own perception: they have the ability to deduce things about their surrounding world through the visual images, sounds and other external sensory inputs just like us humans!

With developments in Deep Learning algorithms, AI is driven forward. The various deep learning algorithms can detect numerous patterns without having any prior definition of these features. And in a broader sense, Machine Learning means the application of any algorithm which can be applied against a set of data to discover a pattern within the same. Such algorithms have features like supervised, unsupervised, classification, segmentation, or regression. Moreover, while they are very popular, there are many reasons why Deep Learning algorithms may not make other Machine Learning algorithms.

Data Science Machine Learning Certification

The 5 major types of problems with AI:

Now that we have some background knowledge, we can now discuss the five major types of problems with AI:

Domain expertise: troubles involving reasoning based on a complex body of knowledge

This consists of tasks that are based on learning several knowledge bodies like financial, legal, and more, and then formulating a process where the machine will be able to simulate as an expert in the given field.

Domain extension: problems surrounding extension of a complex body of knowledge

In this case, the machine learns a complex body of knowledge like information regarding the existing medication and much more, and then suggests new ideas to the domain itself, like for instance new drugs for curing diseases.

Complex planning: projects that require complicated planning

There are many logistics and scheduling projects, which can be done by current (non AI) algorithms. But as optimization keeps developing and gets more complex AI would slowly grow.

Proficient communicator: tasks that involve developing existing communications

AI and deep learning can offer benefits to many communication modes such as intelligent agents, automatic and much more.

Fresh perception: projects that involve a unique perception

Deep learning and AI can be capable of producing newer forms of perception which enables new services like autonomous automotives and more.

Take up a Machine Learning Certification in order to make a change with AI.

 

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