Data is very valuable for any business. It can help in decision making, planning, and more. Here, Business Intelligence comes into action. BI uses various softwares, applications, tools and services that enables access to and analysis of data. This improves and optimizes decisions and performances. Using Business Intelligence can help you to get more value by improving customer service, employee productivity, and more.

Following are the few ways one could get more value from Business Intelligence:

1. Build real-time BI into your customer-facing services
2. Improve employee performance through BI
3. Improve Customer Service
4. Predict new revenue streams
5. Automate budgeting and forecasting
6. Shift the emphasis to analysis
7. Embed BI into other platforms
8. Cut time wasted on data gruntwork
9. Bring unstructured data on board

Read more about them at: https://www.cio.com/article/3254646/business-intelligence/9-ways-to-get-more-value-from-business-intelligence.html

Whether you are a data analyst in a firm or a developer training its machine learning model, you deal with data. Rather you need data! Data is one of the essential things which is needed to create a foundation. The decisions and results are relied on the output you get from the data. Thus, data is important and like every other thing, it also works on the principle of Garbage In, Garbage Out.

Many people make mistake while feeding data to their data set with a hope to get better results.

However, they end up having an ugly dataset with a greater risk of damaging their product.

The 6 most common mistakes are: Not Enough Data, Low Quality Classes, Low Quality Data, Unbalanced Classes, Unbalanced Data, No Validation or Testing.

These mistakes can be fixed which could further help in fetching good results.

One just need to remember that their dataset is equally important to the model they are working on. Without a balanced dataset, getting a fine finish product is next to impossible.

To know how to fix those mistakes visit: https://hackernoon.com/stop-feeding-garbage-to-your-model-the-6-biggest-mistakes-with-datasets-and-how-to-avoid-them-3cb7532ad3b7

Big Data and Analytics are not just confined to online services anymore, they have made their way to changing things offline as well. One of the fields where they have a significant impact is manufacturing. Big Data has been able to perform Predictive Maintenance. It can help in analyzing performance of individual machines. Not just this, they can even help in making improved strategic decisions for better outcomes. Big data analytics are changing manufacturing for the better. The only question is, will you be coming along for the ride? To know more: 

https://www.smartdatacollective.com/how-big-data-and-analytics-are-changing-manufacturing-for-the-better/

The awesome power of Excel lies not only in its processing ability but also in its uniqueness. It is just like oil and water; our lives are governed by Excel from the beginning of office work. Most people will only cautiously sum up the odd columns, but there are many others who can really take advantage of complex calculations and analysis by using the advanced formulae and other analytical tools. So, take some time to play with the formulas and Excel will reward you big-heartedly with some great magic. It will immediately show you error when you use some wrong formulae e.g. it would hourglass for a while before finally concluding that there were not enough resources for that operation. 

Most importantly, did you ever harness the power of Advanced Excel i.e. macro, goal sheet analysis, what-if analysis, forecast sheets, data validation and pivot tables. These unique operations will not only help you to present data in a systematic, but will also help you in taking managerial decisions without wasting much time. Moreover, the knowledge of Advanced Excel will make you valuable in the eyes of your team members and boss. So, Excel is still indispensable in this age of big data.

If you still do not know Advanced Excel, it is better to up skill yourself rather than to do nothing.

Data can be put to use in many ways and it should be explored. Data should be used to its full potential, this is not about technology but management. A team of data scientists employ a series of various analysis to look into the entire series from data insight to profit. There are 7 methods to put data into work. 1) Use data to make better decisions along the chart. 2) Use innovations in products, services and processes. 3) Making existing products more valuable. 4) Improve quality, eliminate costs and build trust. 5) Sell or license richer data. 6) Asymmetries should be exploited. 7) Connect providers and those who need the data. These help to provide greater value to others. Read more at: https://hbr.org/2017/06/does-your-company-know-what-to-do-with-all-its-data

Data Lake helps data scientists by reducing their time taken to gather data and start their real work of data analysis. Copying data physically to one centralized environment may be problematic because storing big data can be costly, copying of data can be prohibited. Metadata describing the data is commonly not copied along with the data and therefore not available to the data scientists. Also, technical and organizational management of a data lake is required. Since data scientists ask for easy and quick data access, a more practical solution to it is a logical data lake. A logical data lake, hides where the data is physically stored and whether it has been copied or not. Logical data lakes can be developed with data virtualization servers such as the Denodo Platform. While, copying and physically storing the data twice is the default approach for the physical data lake, it's optional for the logical data lake. It offers access to data without copying if required and to copy data when needed.  Thus, logical data lake is a better solution for data scientists. Read more at: http://www.datavirtualizationblog.com/data-scientists-physical-data-lakes/

According to a study, it was found that 78% enterprises agree that data strategy, collection and analysis have potential to fundamentally change the way their business operates. The sole aim of an effective data strategy is to utilize this potential . The 5 questions that one need to answer before building a data strategy are : WHAT is Data Strategy?: It is a strategy that allows you to have a comprehensive vision across the enterprise.

WHY do we need a Data Strategy? :You need a data strategy to find correlations across multiple disparate data sources, predict customer behavior, predicting product or service sales

WHEN should I start or have a Data Strategy?: Answer is NOW.

WHO in our organization should drive this Data Strategy?:Chief Data Officer

WHERE do we start with Data Strategy?:It depends on how the organization is structured , it’s recommended to start it in some business unit.

 Read more at : http://dataconomy.com/2017/01/data-strategy-part-i/

There is a big gap between what attackers do and what preventions defenders do to prevent it. The general idea is to build bigger and better blacklists for all the threats known or calculated using better threat intelligence.  We always hunt for easy ways, trying to seek out automation of security infrastructure. But these won't' suffice. The reason is because all the defenses are static and accessible to all. All it takes a hacker is to write a script to bypass these security measures. So a possible solution might be RITA, which stands for Real Intelligence Threat Analysis. Its SANS's free new framework that will help in hunting attackers by extending the traditional signature analysis. Read more at: http://www.darkreading.com/vulnerabilities---threats/introducing-rita-for-real-intelligence-threat-analysis/a/d-id/1323244

In recent years, data mining has been used widely in the areas of science and engineering, such as bioinformatics, genetics, medicine, and electrical power engineering. Several researchers and organizations have conducted reviews of data mining tools and surveys of data miners. These identify some of the strengths and weaknesses of the software packages. They also provide an overview of the behaviors, preferences and views of data miners. How about reaching a few inches deep into the applicability and usability of data mining which is the analysis stage of a typical KDD in terms of Education?

Please Follow this link

http://www.thehindu.com/news/cities/chennai/harnessing-data-mining-to-revolutionise-education/article5690365.ece