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

Business and IT are two completely different fields. Yet there is always a need to mix them for the betterment of both fields. However, there is a huge gap between these two and to bridge them a Business Analysts is comes into role. A business analyst is responsible to take IT and Business together by using data analytics to analyse the ongoing processes and methods, determine plans and requirements and recommend future plans on the basis of current studies. Now days, data is very important for a business. It can help in planning, decision making etc and thus business analysts are a need for an organization. It is important for a business analyst to be good in both hard skills and soft skills. He must be good at sharing the information he was able to figure out with the team. Similarly, he must have a strong IT background.

To know more about a Business Analysts visit: https://www.cio.com/article/2436638/careers-staffing/project-management-what-do-business-analysts-actually-do-for-software-implementation-projects.html

After Artificial Intelligence and Machine Learning, the next most emerging field in todays world is the field of Data Science. It is said to be the cousins of AI and ML and mainly deals with data. It intakes data, uses processes, algorithms and scientific methods to extract knowledge and valuable data from large data sets. This field is need of each and every type of organization. Whether it be business or an IT firm, every organization needs data for improvement. Thus, outcomes from the processing of data are further used for decision making and for improving current functioning.

People often gets confused between Data Science, Data Analytics and Big Data. The key difference between them is that Data Analytics and Big Data are components of Data Science. Data Science extract values from the output of Data Analytics and Big Data to solve problems.
The goal of Data Science is to extract business-focused insights from business. This could help organizations in many ways.

Read more about this topic at: https://www.cio.com/article/3285108/data-science/what-is-data-science-a-method-for-turning-data-into-value.html

In the predictive analytics market for healthcare, advancing Big Data processing tools, data mining and data organization are being increasingly used by market research firms to predict huge gains. Also by leveraging Big Data and scientific advancements, healthcare organizations are now being able to fuel the future of patient care. Despite the fact that there are several roadblocks to using analytics in this field there are also many ways by which Big Data can actually revolutionize the health field. Some of them are listed below as:

• Big Data Boost on Precision Medicine and Research- By tapping reams of data from tools like Smartphone apps, genomics and mobile biometric sensors, precision medicines are now being able to treat individuals with therapies and treatment plans more specific to them
• Tapping Big Data for Real-Time Infection Control- By using “sniffer” algorithms to assign risk scores, the data analytics pilots are identifying patients that are at risk for sepsis. Moreover the Big Data network is being able to predict and prevent infections out of the system thus squeezing the cost out and creating a safer environment for patients.
• Cost Cutting with Patient Data Analytics- Predictive analytics is believed to cut 25 percent or more annual costs of more than 47 percent healthcare organizations over the next five years.

Read More at: https://healthtechmagazine.net/article/2017/11/predicting-analytics-3-big-data-trends-healthcare

A potential threat that all businesses must be aware of is that of cyber attacks. Several data breaches have occurred in the past few years, the Equifax Data Breach in 2017 being the most noteworthy one. As a result, the legal repercussions have grown more stringent tightening the regulations on cyber security. Businesses could face severe consequences which includes consumer lawsuits, criminal charges for evading regulations and potential business shutdown. Consumers have sued companies on the ground of leakage of their confidential information furnished with respective businesses. The Health Insurance Portability and Accountability Act along with few other acts have strongly enforced cyber security regulations. Failure to abide by these brings in huge losses for the business concerned. To avoid such powerful legal ramifications, small and large businesses both must adhere to cyber security regulations.

Read More at: https://www.smartdatacollective.com/3-legal-repercussions-of-cyber-attacks/

Businesses having their analytics processes in place now resort to data monetization to reap benefits to the next level. Data monetization refers to deriving financial benefits from data sources. The largest tech companies nowadays are geared towards delivering the best user experience and collects proprietary data on which researchers perform analysis to derive insights and plan subsequent plan of action. Companies deploy block-chain ledgers to reduce processing loads and specialize in combination of big data and block-chain to bring increased profits. Even the smallest businesses benefit from e-mail monetization by leveraging data collected through the cheapest avenues possible. From managing a blog to small e-mail lists, linking helps in generating leads that turns data into money.

Read More at; https://www.smartdatacollective.com/how-data-monetization-can-add-value-analytics/

Almost 97% of the large companies leverage analytics and such data-driven businesses have emerged to be nearly 6% more profitable as indicated in a study by IDC. Location can be an address, a delivery route, a service boundary or a sales territory and along with disruptive technologies such as machine learning, artificial intelligence, location analytics helps in brisk innovation of the players in the financial realm. It’s required to move towards the analytical maturity curve implementing a 360 degree approach. With the help of location analytics, the real change can be observed between growth of new applications and innovations, taking the trend from static to dynamic content followed by connecting content. There are certain prominent cases where location analytics has helped in increasing profits that include Tamecco, the Tokyo-based company that focuses on customer satisfaction to achieve the moto that location-based analytics assists a lot. Among others are the Grey Jean Technologies and Teemo with the tagline of “Drive-to-Store” engaged in revolutionizing retail advertising. It is expected of industries to benefit from technologies like geospatial intelligence, forecasting and visual analytics.

Read More at: https://www.analyticsinsight.net/how-innovation-leads-to-location-analytics/

The major challenge facing any business organization is to use the massive amount of people data to nurture relevant insights on improving business performances. Analytical techniques varying from reposting to predictive analytics to experimental research help in directing company HRs. Following the comprehensive implementation of the cloud HR systems, companies are using data in all facets of workforce management and operational improvement, moving beyond employee engagement and retention. HR has been emerging as the “intelligent platform” with analytics becoming a critical part of the high-performance hiring in the talent acquisition trend. In order to reduce unconscious bias in hiring, businesses resort to the use of interview data and careful parsing of job posting language. With commensurate technological advancement, analytics will deliver personalized recommendations.

Read More at:  https://www.analyticsinsight.net/using-people-analytics-to-drive-business-performance/

Artificial Intelligence coupled with machine learning algorithms has taken the defenSe industry to great heights by bringing in this paradigm shift from descriptive to prescriptive analytics where soldiers have the knowledge of potential ceasefire beforehand. Data mining and machine learning algorithms help in analyzing unusual movements round the border thus making combat systems more effective at the decision juncture. The US Defense department has approximately spent US$1.6 billion in cyber defense. Although India is not far in the race, it is catching up for the time being. Many India based analytics firms are providing technologies to measure border infiltrations, detect smuggling and possible future agitations. However, India is progressing in the race of cognition of AI with machine learning to give a new shape to the country’s military services.

Read More at: https://www.analyticsinsight.net/data-and-guns-how-ai-is-changing-the-defense-sector/

Big data, AI and Machine Learning has given us an era where technology drives humanity but such digitization comes with certain cons as well. Handling a titanic amount of data seems to be harmless as long as businesses know how to use it and what purpose the data is serving. There should be a thin line between the actual required data and the data that has been irrelevantly collected. Big data has held us high but has failed to keep up with the privacy laws. Major data leaks and cybercrimes are the two prominent threats that growing technology has brought with it. Identity theft, classified as nuisance data breach by the index, has resulted in the loss of nearly 200 million records last year. Data manipulation and data integrity attacks are becoming popular as the world becomes more compact and technology dependent. To ace with this, the company must implement measures such as encryption, user access key etc. Fighting with the big data flood is not a company’s cup of tea as it involves various risks to triumph over. The big data storm must be dealt with carefully with proper planning and execution. 

Read More at: https://www.analyticsinsight.net/big-data-disruption-organizations-need-careful/

To keep track with the increased competition in today’s business environments, business leaders look outward to find potential innovation. Facing competition from start-ups, established businesses are inclined to pursue mergers and acquisitions. Such outward looking attitude might cause leaders to overlook ideas of innovation available internally. With the help of digital collaboration tools, allowing for staff participation in innovation encourages teamwork and camaraderie. Inward looking innovation involves the following few steps:

1) Formalizing the program: Setting up innovation programs on whims leads to major failures and hence, such programs should be formalized with guidelines. Larger companies set up their own incubation centres where their teams operate as start-ups whereas smaller companies enable teams to embark on pet projects.

2) Breaking down data silos: Data silos decelerate growth and innovation programs should break down such silos. Participants should be allowed to tap on a variety of skills and perspectives by using collaboration tools like Trello, Slack and Conceptboard.

3) Track Progress:  There should be a time element in the program lest projects won’t progress. Making events out of program milestones is a good way to inject excitement.

4) Provide executive support: Leadership plays a critical role in any innovation program with the duty of keeping their efforts aligned with the business goals. The momentum should not break down. Proper planning is indispensable for efficient time and workload management. 

Such efforts bring concepts closer to reality and not only seek to generate bright ideas. 

Read more at: https://www.smartdatacollective.com/ways-nurture-employee-innovation-from-within-organization/

In the digital age, social media is the most effective tool that helps improving and promoting any type of business content, get customer feedback and improve the overall reach of the company. This has led to the emergence of social media analytics which involves optimizing analytics and social median data into usable information to interpret how exactly the business content is going with the community of followers. 

A clear definition of the company goals lies at the root of such social media analytics with business objectives varying from prioritizing customer service to establishing a name in a niche or industry. Once a goal is defined, social media analytics could be used to frame strategies for accomplishment of such desired goals. Without a goal in the background, the system might go haywire bringing no effective results.

Being saturated with millions of click bait type contents, generic content does not work for the social media and the marketing world. Social media has restricted amount of characters hence, drawing of these characters should be maximized which helps to see the type of content that resounds best with the followers. Analyzing the increasing leads attained through content makes way for an inflated sales basket for the business. Average content does not acquire any place and being concise and thought-provoking in a post yields better results.

The success of a business lies in the timing of a post. Posting about a particular content at a specific time can increase sales. Also publishing a post on high traffic days can bring in commendable results for the business concerned. 

Read more at: https://www.smartdatacollective.com/use-social-media-analytics-increase-business-success/

Creativity and Innovation are what makes us truly human. Starting with visually representing the stunning features of animal species on paper to making intricate structures on road we can do it all. However such creative and innovative skill set are also present in some AIs who with human like capabilities and behaviours and under the guidance of the programmers have produced original paintings, songs and digital artwork. While some make their own dance moves, others rhyme like Kayne. Few examples of today’s artistic AIs include:

Dance-Dance Revolution- An AI inside a white fabric dome structure, records and processes dance moves performed by people and then incorporates those dance moves inside a “Virtual Dancer” after ‘remembering’ from human dancers.

Blooming Dinos- Creation of “botanical dinosaurs” images composed entirely of flowers and plants by AI.

The stuff of nightmares- By using deep learning, a system of data structures that form connections similar to that of neurons firing in the human brain, an AI project aptly named "Nightmare Machine” makes pictures frightening.

Master Class- An AI named “Vincent” assists humans to produce digital creations of canvasses of some of the most celebrated painters of the 19^th and 20^th centuries.

Read more at: https://www.livescience.com/62713-can-machines-be-creative-ai-humans.html

The buzzwords in technology are no new to someone. Whether it be Artificial Intelligence, Machine Learning, Data Sciences or Analytics, each of these are invading in our lives promising us better future. However, it is believed that expertise interested in data sciences are not widely spread. Data Sciences is a field that can improve business, can help in other technological fields, can help in decision making and more. 

It is rightly said that prevention is better than cure. A wrong step in data sciences can affect the decisions and the results. One should avoid the following mistakes while dealing with data:

1. Assuming your data is ready to use and all you need
2. Not exploring your data set before starting work
3. Not using control group to test your new data model in action
4. Starting with targets rather than hypotheses
5. Automating without monitoring the final outcome

To study mistakes like these read https://www.cio.com/article/3271127/data-science/12-data-science-mistakes-to-avoid.html?nsdr=true