Global markets are becoming more complex each day, and therefore, it has become essential for business intelligence teams to apply advanced methods for data interpretation. They believe that only the decisions based on quantitative data can be justified. Although there are some ways quantitative research may go wrong, the truth comes out only when you meet people, talk to them, involve them in creative exercises.
Read more at: http://www.dataversity.net/science-big-data-art-interpretation/
Data is the backbone of analytics and machine learning and hence one of the most important tasks in analytics is to get the right kind of data and in the required format.The importance of data can be understood by the fact that around 60 to 80 percent of the time of an analyst is spent in preparing the data.
What exactly is data preparation? In a nutshell, it is the process of collecting, cleaning, processing and consolidating the data for use in analysis. It enriches the data, transforms it and improves the accuracy of the outcome.
How is it done? It is mostly done through analytics or traditional extract, transform and load (ETL) tools. ETL tools include self-service data preparation tools, data cleansing and manipulation tools, etc.
Since data is the foundation of the analytics, right data will helps in analysing the situation better and help organizations in reacting positively to the market shifts.
To know more read the full article by Ashish Sukhadeve (business analytics professional) at: http://www.datasciencecentral.com/profiles/blogs/why-data-preparation-should-not-be-overlooked
Modern needs of Big data consumption require data integration before data actually hit the business intelligence tools. This includes leveraging complex and unstructured data and enables raw data to flow securely through business. Today, even the smallest companies produce huge amount of data across systems which need to communicate with each other and therefore requires a platform to pipe all these data sources into Data Lakes.
Read more at: http://www.dataversity.net/dont-put-cart-horse-comes-big-data/
It has been evident that a great customer experience is one of the signs of a healthy business model. Machine Learning and Data Analytics are playing a fundamental role in building consumer intelligence systems. It is important to capture data and there is no single magic source to collect data. Telecoms are making billions by selling data. You need to ensure that the data is relevant to business. Once you have the right data, you are ready to model, design and engineer and deploy your 360-degree customer view platform and achieve the enhance customer experience for your organization.
You can read more at: http://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A508502
Analytics is one of the of the most hot topic of the 21st century and it’s starting to become the second currency to various organisation, but despite having so much knowledge we prone to create some blunders , they are broadly categorised as Data Visualization Errors (Erroneous Graphs) and Statistical Blunders.
Data Visualization Errors (Erroneous Graphs): This is one area that can give a nightmare to both the presenter as well as the audience. Incorrect data presentation can screw the intuition and can also lead to misinterpretation of data by the audience and can leave the organisation with results which are practically useless for them.
Statistical Blunders Galore: This is probably a “no blunders zone” where one would not want to make false assumptions or erroneous selections and is easily one of the most error prone section. Statistical errors can be a costly affair to both the organisations as well as the audience, if not checked or looked into it carefully and hence must.
To know more read the full article by Sunil Kappal (author) at :http://www.datasciencecentral.com/profiles/blogs/the-most-common-analytical-and-statistical-mistakes
Data Science has become an inevitable charter in our everyday lives where every action of ours is measured, plotted, classified and logged. Businesses have also realized that they should adopt and embrace these changes now or risk being left behind in this fast moving digital world. Data Monetization is the new paradigm for organizations and slowly but steadily data is becoming their currency of trade.
Data Science is more like an art of turning data into actionable insights. Though we consume data regularly, we never cared to look behind the scenes on the rigorous processes, data preparation and machine learning algorithms that give us accurate data to devour. And this looks like some deep mystery but in reality it’s not a mystery, it’s just an intelligent use of data and various resources available to so called wizards: Data Scientists. To know more read the complete article by Prakash Pasupathy at: http://www.datasciencecentral.com/profiles/blogs/solving-the-data-science-mystery
In today’s digital age the world has become smaller.Gone are the days, when organizations used to load data in their data warehouse overnight and take decision based on BI, next day. Today organizations need actionable insights faster than ever before to stay competitive.With real-time analytics, the main goal is to solve problems quickly as they happen, or even better, before they happen. The lead role in revolutionizing real-time analytics is played by Internet of Things(IoT) . Now, with sensor devices and the data streams they generate, companies have more insight into their assets than ever before.
But it is so great as it looks , indeed it is as it helps getting the right products in front of the people looking for them, or offering the right promotions to the people most likely to buy using the real time recommender system.
are the days of waiting long hours to know the analytics of your data , now is the time to move beyond just collecting, storing & managing the data to take rapid actions on the continuous streaming data – Real-Time!! You can read the full article at http://www.datasciencecentral.com/profiles/blogs/do-you-know-what-is-powerful-real-time-analytics
Today world has become smaller and faster, with increasing computation speed decisions are done in seconds instead of days. Product information and comparison is available on any device any time. Real Time analytics involve solving problems quickly as they happen or even before they happen. Companies now have more insights into their assets. Several industries are using streaming data and putting real time analytics. The big data revolution has further accelerated the demand of real time analytics to analyze customer behavior. Gone are the days when decisions were based on data stored on a disk , actions are taken on streaming data. Read more at: http://www.datasciencecentral.com/profiles/blogs/do-you-know-what-is-powerful-real-time-analytics
A traditional data science department is comprised of Data Scientists, Data Engineers and Infrastructure Engineers. This model has a drawback that one role is always dependent on other and likely to criticize them for task failures because they didn't do their job well. These conflicts may reflect in the quality of final data product. So, what went wrong? You probably don't have big data. Jeff Magnusson (Director of Algorithms Platform at Stitch Fix) suggested a clever approach of forming a "High Functioning Data Science Department" which involves building an environment which allows autonomy, ownership, and focus for everyone involved yet at the same time clearly distinguishing the roles of Data Scientists and Data Engineers. Data scientist can't suddenly become talented engineers nor is that engineers will be ignorant of all business logic, the partnership is inherent to the success of this model. You can read more at: http://multithreaded.stitchfix.com/blog/2016/03/16/engineers-shouldnt-write-etl/
Customers love personal touch and feeling special, whether it’s being greeted by name when we walk into the store, a shop owner remembering our birthday It make them feel like they are your single most important customer. But in an online world, you can’t guide them through the product they may like. This is where recommendation engines do a fantastic job.
With personalized product recommendations, you can suggest highly relevant products to your customers at multiple touch points of the shopping process. Intuitive recommendations make them feel like your shop was created just for them and hence they become your regular customers.
Application of Data Science to analyze the behavior of customers to make predictions about what future customers will like and understanding the shopper’s behavior on different channels can increase the sale by over 30%.Ultimately most important goal for any organisation is to convert visitors into paying customers and hence product recommendations are extremely important in digital age.You can read the full article on Product recommendations in Digital Age by Sandeep Raut (Author) at: http://www.datasciencecentral.com/profiles/blogs/product-recommendations-in-digital-age
In a short span of five years the world of analytics has changed immeasurably. Now we see fast analytics, interactive experimentation with data and exploratory analysis of data.
But why ? The answer to this question can be summed in three simple points. First, with fast analytics, it’s easier to keep up in an ever-changing world and keep pace with customers and market forces and businesses can see a measurable value from running advanced analytics on their data. Second, due to low prices of analytics businesses must meet customers’ expectations or risk losing them to a competitor. Third, it has the ability to elevate a company to the next level and provide it with a competitive edge over its rivals through the real-time insights it can achieve.
And , hence every one in this competitive market is shifting to advance analytics. To know more you can read the article by Aaron Auld (CEO of EXASOL) at: http://www.datasciencecentral.com/profiles/blogs/the-rise-of-advanced-analytics .
Robo-advisers are automated advisers with provide financial advisory as low cost, so it’s available to everyone. The costs are as low as 1 euro. They open the door to the financial markets and give you the possibility to invest in stocks, bonds and other securities and keep their costs low by trading Exchange-Traded Funds.
But, how do they exactly work ?? Robo-advisors use algorithms based on mean-variance optimization, a mathematical framework to create a portfolio of assets such that the expected return is maximized for a given level of risk. Financial market data is used to estimate expected return, standard deviation and correlation for every asset class. On opening an account, you are asked simple questions about your age, income, savings and willingness to take risk. This data is collected to estimate your risk tolerance and fit their model to your current situation and preferences and give you the best advice to invest in the market. To know more read this article http://www.datasciencecentral.com/profiles/blogs/robo-advisers-and-the-future-of-financial-advice by Stefan.
Aim-Lever-Data-Implement (ALDI) is an approach to integrate marketing analytics with Data Science, i.e making data the primary object of various decisions. So is it something very difficult or some kind of rocket science , no it’s a simple paradigm which follow the following approach :
Aim :
The aim of the analysis needs to be fixed by the strategy teams, before any data scientists gets involved, as they are are ones who know what exactly is needed.
Yes, if companies need anything in 2017 they are Data Scientists.
But why , what is so special about them? And the answer is :
Data scientists tracks millions of data sets and provides concrete information for organizations looking to break their data into meaningful information that can be used at all levels in the organization.
As this is the data century , every company wants to recommend its users what they are most likely to choose and hence the need of Data Scientists to study the data and extract various pattern from it and hence creating a 360-degree view of their customers. This not only impresses the customers but also helps the companies in understanding their customers better and hence improving their services according to the customers.
So , in a nutshell yaa companies do need Data Scientists.
What is dark data ? The large amount of data collected by companies that goes useless due to lack of analysis(39%) or structure(25%) or even sometimes due to lack of proper tools(13%) is known as the dark data. If used/analyzed properly gives a new dimension to the companies.
But how can we harness the dark data. Well it’s no rocket science , just some simple measures and you have a whole new dimension of data.Here are just a few of them :-
Keep a track of user logins and various checkout at different locations, this helps in creating a 360-degree view of the user.
Mobile phone data, this will help to illuminate an array of new product and marketing opportunities, and hence improve marketing effectiveness.
Data Science is one of the hottest job of the 21st century. But is it easy to be a data scientist , and the answer is it’s not hard. But here are some of the things that one need to avoid to be a bad data scientist :
Focus on tools rather than business problems :
Yaa tools do matter, but what is even more important is the problem you are working on, it should be the basis of all your decisions.
Converting analytics to action is a nightmare as it is fraught with problems, of which, not the least is the political nature of organizational decision-making. When it comes to ROI, we see lots of organizations taking on the analytics bandwagon and are happy to have more insight into which pages, and segments are driving returns. A host of digital tools can transform data into pretty dashboards to help improve ROI.
Overall, businesses do a great job of monitoring, understanding, and utilizing ROI information–translating it into effective actions. These relatively simple metrics guide managers on which messages, segments, products, and channels are working their best, allowing them to tweak strategy in order to improve market performance and intelligence. Still, sometimes issues of data quality and politics impede implementation of the right actions based on available data.
Data is the latest weapon in the market and is the deciding leader in almost all the industries, especially consumer packaged goods, or CPG. The key reason being, the fast changing consumer preferences and influx of larger pool of 21st century competition, primarily the retailers – both, brick & mortar and online.
However, in spite of this gigantic pile of data generated every single day, the plight of CPG companies is similar to a soldier who is given a latest automatic machine gun but doesn’t know how to effectively use it. There are companies who just don’t know what to do with the data. Those who know, are, perhaps, not using it efficiently.
To know more of effective use of data, read on : http://blog.fractalanalytics.com/big-data-visualization/what-shapes-your-analytics-maturity-pathway/
In recent times, marketing has become an integral part of any business. Your business may offer the best products or services in the industry, but without continuous projection of the product to the customers, the chances of your competitors taking over your products is very high.
In the early 1950s and 1960s, marketing was production oriented and the quality of the production was the driving factor of marketing. Later, as new production technologies started to develop, techniques evolved simultaneously to meet the needs of the customers and efforts were made to maximize customization. But the next major advancement in marketing is literally hacking the brain of the customer.
Neuroscience is the field of study where the response to products and consumer decision-making is understood at the level of body and mind. The Neuromarketing concept is based on a model wherein the major thinking part of human activity, including emotion, takes place in the subconscious area that is below the levels of controlled awareness. For this reason, the perception technologists of the market are very tempted to learn the techniques of effective manipulation of the subconscious brain activity.
Neuromarketing is a flexible method to determine customer preferences and brand loyalty, because it can apply to anyone who has developed an opinion about a product or company.
How do companies attract and engage new clients while making sure the existing clients are happy? The simple answer to this question is, manage.
However, you will not find many executives who would respond to this conjecture candidly. You will, however, find plenty of companies that take the “we manage” approach with one of its most valuable assets: relationships.
While running any enterprise technology company, we’ve seen just how consistently data can be used to help improve sales. But we do know, all its good intentions to provide sales managers with a way to monitor pipeline and sales activity, CRM is still hugely inefficient. Representatives are required to spend time manually entering data, and then spend more time searching through it. While senior management clearly values the ability to monitor these representatives through CRM, the vast majority of sales people dislike the extra work and overhead it creates and generally use CRM begrudgingly – and rarely to its full potential.
Read more at : http://www.business2community.com/strategy/relationship-intelligence-companies-acquire-01702402#tAmo7XUOWfJJ6g3Z.97
