4 Big Data Strategies You Can't Go Without

|

article image
When it comes to customer relationships, big data can usher in big opportunities or big problems. That’s why it’s vital for organizations to take a strategic approach to big data. They must clean their data, integrate it, maximize data value, comply with security and governance requirements, and make sure the right people have the right access to the data at the right times.

Spotlight

Global Bridge InfoTech Inc.

Global Bridge Info Tech(GBIT) is an NXT-GEN IT service provider with Global experience in IT consulting, System Integration and Remote infrastructure managed services(RIMS). We are indispensable partner for IT organizations to embrace “Internet of Everything (IOE)” with service offerings in mobile, cloud, analytics and social services. Our Global Service Delivery Framework has helped customer to transform IT Services landscape with consistent YoY cost savings.

OTHER ARTICLES

MiPasa project and IBM Blockchain team on open data platform to support Covid-19 response

Article | April 1, 2020

Powerful technologies and expertise can help provide better data and help people better understand their situation. As the world contends with the ongoing coronavirus outbreak, officials battling the pandemic need tools and valid information at scale to help foster a greater sense of security for the public. As technologists, we have been heartened by the prevalence of projects such as Call for Code, hackathons and other attempts by our colleagues to rapidly create tools that might be able to help stem the crisis. But for these tools to work, they need data from sources they can validate. For example, reopening the world’s economy will likely require not only testing millions of people, but also being able to map who tested positive, where people can and can’t go and who is at exceptionally high risk of exposure and must be quarantined again.

Read More

Splunk Big Data Big Opportunity

Article | April 1, 2020

Splunk extracts insights from big data. It is growing rapidly, it has a large total addressable market, and it has tremendous momentum from its exposure to industry megatrends (i.e. the cloud, big data, the "internet of things," and security). Further, its strategy of continuous innovation is being validated as the company wins very large deals. Investors should not be distracted by a temporary slowdown in revenue growth, as the company has wisely transitioned to a subscription model. This article reviews the business, its strategy, valuation the sell-off is overdone and risks. We conclude with our thoughts on investing.

Read More

A BRAND NEW CHIP DESIGN WILL DRIVE AI DEVELOPMENT

Article | April 1, 2020

The world is now heading into the Fourth Industrial Revolution, as Professor Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, described it in 2016. Artificial Intelligence (AI) is a key driver in this revolution and with it, machine learning is critical. But critical to the whole process is the need to process a tremendous amount of data which in turns boosts the demand for computing power exponentially.A study by OpenAI suggested that the computing power required for AI training surged by more than 300,000 times between 2012 and 2018. This represents a doubling of computing power every three months and two weeks; a number that is significantly quicker than Moore’s Law which has traditionally measured the time it takes to double computing power. Conventional methodology is no longer enough for such significant leaps, and we desperately need a different computing architecture to stay ahead in the game.

Read More

5 Predictive Data Analytics Applications

Article | April 1, 2020

According to Google trends, predictive data analytics has gained a significant amount of popularity over the last few years. Many businesses have implemented predictive analytics applications to increase their business reach, gain new customers, forecast sales, and more. Predictive Analytics is a type of data analytics technology that makes predictions with the help of data sets, statistical modeling, and machine learning. Predictive analytics uses historical data. This historical data is fed into a mathematical model that recognizes patterns and trends that are then applied to current data to forecast trends, practices, and behaviors from milliseconds to days and even years. Based on the parameters supplied to them, organizations find patterns within that data to detect risks, opportunities, forecast conditions, and events that would occur at a particular time. At its heart, the use of predictive analytics answers a simple question, “What would happen based on my current data and what can be done to change the outcome.” In the current times, businesses have multiple products offerings at their disposal to choose from vendors of big data predictive analytics in different industries. They can help these businesses leverage historical data discovering complex data correlation, recognizing patterns, and forecasting. Organizations are turning to predictive analytics to increase their bottom line and gain advantages against their competition. Some of those reasons are listed below: • With the growing amount and types of data, there is more interest in utilizing it to produce valuable insights • Better computers • An abundance of easy to use software • Need of competitive differentiation due to tougher economic conditions As more and more easy-to-use software have been introduced, businesses no longer need statisticians and mathematicians for predictive analytics and forecasting. Benefits of Predictive Analytics Competitive edge over other businesses The most common reason why multiple companies picked up predictive analytics was to gain an advantage over their competitors. Customer trends and buying patterns keep changing from time to time. The ones who can identify it first will go ahead in the game. Embracing predictive analytics is how you will stay ahead of your competition. Predictive analytics will aid in qualified lead generation and give you an insight into the present and potential customers. Business growth Businesses opt for predictive analytics to predict customer behavior, preferences, and responses. Using this information, they attract their target audience and entice them into becoming loyal customers. Predictive analytics gives valuable information about your customers such as which of them are likely to lapse, how to retain them, whether you should market directly at them, etc. The more you know about them, the stronger your marketing will become. Your business will become the leader in predicting your customer’s exact needs. Customer satisfaction Retaining existing customers is almost five times more difficult than acquiring new ones. The most successful company is the one that invests money in retaining those customers as much as acquiring new ones. Predictive analytics helps in directing marketing strategies towards your existing customers and get them to return frequently. The analytics tool will make sure your marketing strategy caters to the diverse requirements of your customers. Personalized services Earlier marketing strategies revolved around the ‘one size fits all’ approach, but gone are those days. If you want to retain and acquire new customers, you have to create personalized marketing campaigns to attract customers. Predictive analytics and data management help you to get new information about customer expectations, previous purchases, buying behaviors, and patterns. Using this data, you can create these personalized marketing strategies that will help keep up the engagement and acquire new customers.   Application of Predictive Analytics Customer targeting Customer targeting divides the customer base into different demographic groups according to age, gender, interests, buying, and spending habits. It helps companies to create tailored marketing communications specifically to the customers who are likely to buy their products. Traditional techniques do not even come close to identifying potential customers as well as predictive analytics does. The major constituents that create these customer groups are: • Socio-demographic factors: age, gender, education, and marital status • Engagement factors: recent interaction, frequency, spending habits, etc. • Past campaign response: contact response, type, day, month, etc. The customer-specific targeting for the company is highly advantageous. They can: • Better communicate with the customers • Save money on marketing • Increase profits Customer churn prevention Customer churn prevention creates major hurdles in a company’s growth. Although it has been proven that retaining customers is cheaper than gaining new ones, it can become a problem. Detecting a client’s dissatisfaction is not an easy task as they can abruptly stop using your services without any warning. Here, churn prevention comes into the picture. Churn prevention aims to predict who will end their relationship with the company, when, and why. The existing data sets can help develop predictive models so companies can be proactive to prevent the fallout. Factors that can influence the churn are as follows: • Customer variables • Service use • Engagement • Technicalities • Competitor variables Using these variables, companies can then take necessary steps to avoid the churn by offering customers personalized services or products. Risk management Risk assessment and management processes in many companies are antiquated. Even though customer information is abundantly available for evaluation, it is still antiquated. With advanced analytics, this data can be quickly and accurately analyzed while maintaining customer privacy and boundaries. Risk assessment thus allows companies to analyze problems with any business. Predictive analytics can approximate with certainty which operations are profitable and which are not. Risk assessment analyzes the following data types: • Socio-demographic factors • Product details • Customer behavior • Risk metrics Forecast sales Evaluating the previous history, seasonality, and market-affecting events make revenue predicting vital for a company’s planning and result in a company’s demand for a product or a service. This can be applied to short-term, medium-term, and long-term forecasting. Predictive models help in anticipating a customer’s reaction to the factors that affect sales. Following factors can be used in sales forecasting: • Calendar data • Weather data • Company data • Social data • Demand data Sales forecasting allows revenue prediction and optimal resource allocation. Healthcare Healthcare organizations have begun to use predictive analytics as this technology is helping them save money. They are using predictive analytics in several different ways. With the help of this technology, based on past trends they can now allocate facility resources, optimize staff schedules, identify patients at risk, adding intelligence to pharmaceutical and supply acquisition management. Using predictive analytics in the health domain has also helped in preventing cases and risks of developing health complications like diabetes, asthma, and other life-threatening problems. The application of predictive analytics in health care can lead to making better clinical decisions for patients. Predictive analytics is being used across different industries and is good way to advance your company’s growth and forecast future events to act accordingly. It has gained support from many different organizations at a global scale and will continue to grow rapidly. Frequently Asked Questions What is predictive analytics? Predictive analytics uses historical data to predict future events. The historical data is used to build mathematical model that captures essential trends. That predictive model is based on current data that predicts what will happen next or suggest steps to take for optimal outcomes. How to do predictive analytics? • Define business objectives • Collect relevant data available from resources • Improve on collected data by data cleaning methods • Choose a model or build your own to test data • Evaluate and validate the predictive model to ensure How does predictive analytics work for business? Predictive analytics helps businesses attract, retain, and grow their profitable customers. It also helps them in improving their operations. What tools are used for predictive analytics? Some tools used for predictive analytics are: • SAS Advanced Analytics • Oracle DataScience • IBM SPSS Statistics • SAP Predictive Analytics • Q Research { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What is predictive analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Predictive analytics uses historical data to predict future events. The historical data is used to build a mathematical model that captures essential trends. That predictive model is based on current data that predicts what will happen next or suggest steps to take for optimal outcomes." } },{ "@type": "Question", "name": "How to do predictive analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Define business objectives Collect relevant data available from resources Improve on collected data by data cleaning methods Choose a model or build your own to test data Evaluate and validate the predictive model to ensure " } },{ "@type": "Question", "name": "How does predictive analytics work for business?", "acceptedAnswer": { "@type": "Answer", "text": "Predictive analytics helps businesses attract, retain, and grow their profitable customers. It also helps them in improving their operations." } },{ "@type": "Question", "name": "What tools are used for predictive analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Some tools used for predictive analytics are: SAS Advanced Analytics Oracle DataScience IBM SPSS Statistics SAP Predictive Analytics Q Research" } }] }

Read More

Spotlight

Global Bridge InfoTech Inc.

Global Bridge Info Tech(GBIT) is an NXT-GEN IT service provider with Global experience in IT consulting, System Integration and Remote infrastructure managed services(RIMS). We are indispensable partner for IT organizations to embrace “Internet of Everything (IOE)” with service offerings in mobile, cloud, analytics and social services. Our Global Service Delivery Framework has helped customer to transform IT Services landscape with consistent YoY cost savings.

Events