Category: Big Data

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To be honest, designing business marketing campaigns for existing customers is as difficult as nailing jelly to a tree. That’s given.
To accomplish this feat of tough analysis, access to a list of customers, email addresses, and purchase data is imperative, but the tricky part remains unattended- sending meaningful insights that will boost a customer’s lifetime value and trigger a repeated purchase.

This trick of accurately targeting customers has been cracked by big enterprises with the help of in-house Data Science teams using a particular approach. The name of their approach is- Market Basket Analysis (MBA).
It is one of the key approaches adopted by renowned retailers to unravel the link between items. One of the basic principles of this approach is to track the combination of items that occur together repeatedly in the transactions.
In simple words, it enables retailers to determine the relationship between the items that consumers purchase. Let’s dig deep for a better understanding.

At the core

The very base of MBA, popularly known as Affinity Analysis, is depended on Data Mining, which uses Association Rule Learning to determine the bond between customers and the attributes associated with them.

The more common and stronger a relationship is, the quicker you can put your customers into segments for future analysis. All that is needed for this initiative is customer and order data.

A scenario– In a grocery store, there are numerous products, out of which consumers can lay their hands on any particular group of things. Say Peanut butter and jelly, cream cheese and turkey, etc. Using Market Basket Analysis on the grocery store data, it will be a piece of cake to determine which products are brought together by customers. Adding a feather in its cap is- discovering new bonds between customers and newer product combinations.

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To comprehend the relationship between Customer Buying Behavior and MBA in detail, read on.

Comprehending Customer Buying Behavior using MBA

With MBA comes the good news of unearthing the connections between the purchasing pattern of customers, by determining the products or menu items that appear frequently in transactions.

Smart retailers can evaluate this relationship between the products, which consumers purchase and can use this data to come up with new products or pricing models for maximum revenue.

Here are some smart ways to make use of such insights from MBA:

Cross Selling - Up sell - Market Basket Analysis

  • Cross-Sell: Group products which customers buy often from the store
  • Marketing promotions: Focus marketing campaigns to customers and lure them to buy products for an item recently bought
  • Web stores- Propose associated items that are frequently purchased together (“Customers who purchased this product, also viewed this product”)

To read the maximum benefits of this analysis, hiring the services of an expert in the field is the only option.

How SPIN’s MBA Analysis expertise helps

It comes as no surprise that MBA is applied to different segments of the retail sector to pump up sales and open new streams of revenue sources by determining the requirements of the customer and making purchase offers to them.

Banking on this theory, SPIN uses MBA to help its clients with:

Cross-Selling: A sales technique that enables the seller to suggest a related product to a customer after the first purchase is made. With SPIN’s MBA, retailers can comprehend consumer behavior and pitch the right product for cross-selling.Up Sell Cross Sell Market Strategy

Product Placement: It is a technique to place complementary and substitute goods together for the customer to buy them together. Using SPIN’s MBA, retailers can determine the goods, which a customer is more likely to buy together.

Detection of fraud: MBA contains credit card usage details and it can be used to determine the purchasing behavior to detect fraud possibility. SPIN’s MBA will prevent your retailer business from such adversities too.

Customer Behavior: SPIN’s MBA helps to comprehend customer behavior under a host of different conditions, enabling the retailer to determine the connection between two products, which people purchase, and get the knowledge of the customer’s buying behavior.

SPIN’s MBA is the combo of AI and ML

Businesses want to evaluate the different angles of customer behavior inside a store. With the right data sets to determine customer behavior of retail stores, businesses can categorize data to define the :

  1. Right product association
  2. Trip types
  3. Point of sale and marketing

Artificial Intelligence and Machine Learning

After analysis of the consumer behavior inside a retailer store, AI and ML techniques powered by SPIN Strategy algorithms are applied to reap the following benefits for the retail business:

  1. Develop lucrative combo offers
  2. Place associate products together in the store
  3. Customize the layout of the eCommerce site catalog
  4. Manage inventory based on the products with better demand
  5. Categorize different shopping trips to generate the best shopping experience
  6. Create customer profiling and apply segmentation using buying pattern
  7. Determining the best product association

To wrap it up

Market Basket Analysis is used by some of the biggest companies in the world to make informed and strategic business decisions. 

Here at SPIN Strategy, our professionals can help you perform such an analysis on your customer base to drive your market growth and design your product.

Interested in exploring MBA? Visit: https://www.spinanalyticsandstrategy.com/

Artificial Intelligence – Leading the way towards global development!

With the world rapidly evolving, customers have become almost equivalent to business owners dictating the shopping terms, sales funnel, marketing efforts, etc. As per their needs, customers decide:

  • How to shop
  • Where to shop, and
  • How to proceed with the transactions-online or offline

Along with adapting these changes in customer aspirations, organizations also need to redefine business operations for every industry to emerge more customer-centric and tailor-made as per customer needs.

Reality CheckCustomer-centricity is not an out-of-the-box topic for organizations, principally for those trying to set a benchmark for client satisfaction.

Before we get further, let’s get an in-depth understanding of what a Customer-Centric company is.

What is a Customer-centric company?

In business terms, being a customer-centric company means offering unprecedented customer experiences right from awareness to purchasing, and finally post-purchase stage.

Customer Centric Business Structure 2020

For a customer-centric company, it is a mandate to execute business operations that stimulates positive customer experiences, prior and post-sales conclusion. This assures to uphold the culture of repeating customers, and boosts consumer loyalty for increased profits.

Does your business is in need of it?

Business needs Customer-centricity: Why

Market hypothesize confirms that businesses that apply customer-centric strategies encounter a 55% increase in profits, year by year. For such businesses, customer-centricity is more than mere words on a paper and goes beyond employee meetings or customer surveys.

If the target is to improve the business value and be in sync with the changing trends, incorporating customer-centric strategies is the key.

As per the core principle of customer-centricity, every organization in any industry requires a robust foundation in 1) leadership and 2) strategy, and the poised use of 3) people, 4) platforms and 5) processes.

How to be a customer-centric business?

Customer-centricity commences with a company’s culture and commitment to customer’s success.

Here is how your organization can turn into a fully-fledged customer-centric business.

Customer Centric Business Strategy

1. Predict customer requirements

Anticipating the market’s future needs is a game-changing business move. To be customer-centric, it is imperative for companies to anticipate customer needs and provide helpful suggestions accordingly.

2. Compile customer feedback

Frequent and regular communication with customers is the key to successful customer-centricity. Thanks to the digital transformation of the world, there are countless encounters to accumulate customer feedback like emails, chat, In-app messages, SMS, FB messenger, etc.

3. Convenient customer support

Contacting the support team of a digitally built business is the most difficult task. Easy access to customer support builds rapport and trust, laying the foundation for long-term business relationships.

4. Deliver proactive customer service

Providing customers with added value beyond the point of purchase, like proactive customer service- resources to customers to solve minor problems independently, is the key differentiator.

5. Move beyond purchase

Only with added benefits that extend beyond the point of purchase can old customers be motivated to  buy again, and create a memorable experience.

 

Measuring customer-centric success is crucial: 

Customer Centric Business Strategy
Customer Centric Business Strategy

To measure the success of a customer-centric business, taking into account the following top three significant customer metrics is fundamental:

Net Promoter Score

Are you happy with the products/services?

NPS that focuses on unraveling customer loyalty uses the answer to define the success of customer-centricity.

Whenever a client answers to this question, the response is segregated into certain predefined criteria.

 Net Score Business

Promoters (9-10) – Such customers love the product/service and are quite likely to refer it. Customers who rate your product/service a 9 or 10 have high customer lifetime value and are indefinitely repeat customers.

Passives (7-8) – Such customers who rate your product/service a 7 or 8 are simply happy being a client, however, they are more prone to switching to a competitor in case of a better and inexpensive product.

Detractors (0-6) – Customers providing such ranking are unhappy with the product/service, and are more prone to damaging brand reputation.

Bottom line- The more Promoters a business has, the better the growth it experiences.

Churn Rate

Procuring new clients on a frequent basis puts most organizations in a quandary. Thus, more business is investing in retaining existing clients instead of chasing more fresh leads. Here’ why:

  • Procuring new clients, compared to existing clients, increases the cost up to 5 times.
  • Minimum 2% hike in customer retention is equivalent to cost-cutting by 10%.
  • On an average, companies lose around 10% of their customer base annually.

Note: Companies with a higher retention rate grow faster.

Customer Lifetime Value (CLV)

For any growing business, the customer base is one of the most valuable assets.

With CLV, the revenue collected from such clients during their customer lifetime, starting from the first purchase until the time transactions stop is measured.

With the help of CLV final calculations, it is easy to comprehend why a business should keep investing in customers.

Concluding thoughts

It is no news that businesses are reaching out to different ways to incorporate more customer-centric strategies in their approach.

Any organization that has a lack of customer data and priorities revenue over customer needs still has some catching up to do to sustain in the 21st– century business world.

Introducing the customer-centric brand culture is undoubtedly the best route for businesses to align leaders’ choices with the mindset of the team and cultivate the values. To successfully apply the customer-centric strategy, this culture must be in sync with other business strategies.

At SPIN, we maintain robust individual contact profiles and useful CLV insights and purchase likelihood tools that can help your esteemed organization to be customer-centric successfully, and market smarter.

Visit today: https://www.spinanalyticsandstrategy.com/

 

PREDICTIVE MODELS 2020

Be more accurate towards forecasting your data. Know the hidden patterns within your data to explain statistical abnormalities and explore the relation behind the unexpected Customer Patterns and sudden fluctuations in the audience segmentation metrics. 

Here are a few Predictive Models that make it happen:

1. CUSTOMER SEGMENTATION: 

Dividing customers into groups based on common characteristics is Customer Segmentation.  

Knowing your customers and targeting them in the most relevant way to boost Customer Retention and Customer Satisfaction is the primary motive for any business.

Get Answers for every doubt you have related to your customers using this Model:

  •  Know your customer requirement before launching a new product
  • Target specific Customer Branding
  • Rebrand new customers

Customer Demographics, Customer Buying Behavior, Customer Interests, and Customer Profiling are some of the additional benefits of the Customer Segmentation model that businesses look forward too.

2. CROSS SELL AND UP SELL MODELS:

Looking for quick wins and easy growth?

Upselling and cross-selling are considered to be the two most effective ways to boost revenues for retailers. Upsell is used when business owners want to convince their customers to purchase a more expensive  product/ service, while salespeople use cross-sell to encourage consumers to buy more products based on instinct.

Here is the strategy used by Cross-Sell and Up-Sell model:

  • Loyal Clients Feedback First
  • Business Quarterly Reviews
  • Gathering testimonials and Case studies
  • Blog sharing
  • Trial and error
  • Time to time discounts and Offers
  • Know your Needs
  • Employee Retention

3. CHURN MODELS:

Customer Churn Prediction

Churn Rate defines the percentage of Customer Retention for your products/services. Customer Churn prediction tasks will often use customer data to determine:

  •  Time spent on a company website
  •  Products/ services  purchased
  •  Demographic information of users
  •  Links clicked
  •  Text analysis of product reviews

Types of churn Models:

  1. Define Metrics with Consumer data
  2. Shifting insights based on Outputs
  3. Adding machine Learning to Churn

4. SENTIMENT ANALYSIS:

Discover Customer emotional connect towards a product/service and boost Brand Reputation Management with Sentiment Analysis.

Sentiment analysis and opinion mining find numerous applications in e-commerce, marketing, advertising, politics, and research, with:

  • Text Polarity
  • Sentiment ranking
  • Feature Sentiment Analysis

5. PRICE PLANNING AND ANALYSIS:

It is important to analyze the pricing situation to develop a Price planning and Analysis strategy to:

  • Evaluate new product ideas
  • Test marketing
  • Introduce strategy
  • Add Positioning               

Analyzing the pricing strategy benefits the business by:

  1. Establishing the responsiveness of the market to price
  2. Determining cost
  3. Analyzing competition
  4. Assessing legal constraints

6. CUSTOMER SPENDING PATTERN ANALYSIS: 

Customer spending patterns can be divided into regular spend pattern and lifestyle spend patterns.

  • Regular spending  means basic necessities of life, 
  • Lifestyle spending means spending on a computer, internet, car, cell phone, etc.
  1. Cultural
  2. Social
  3. Psychological
  4. Personal

7. PRODUCT RECOMMENDATION:

Product recommendations will work for ECommerce Businesses where there are unfriendly sales assistants to help customers with each step of their shopping journey.

Product recommendation engines fall into 2 main categories: 

  1. Unpersonalized
  2. Personalized

8. IMPACT ANALYSIS OF SALES PROMOTION:

What are the promotional offers that has the highest impact at present?  This question is relevant for every product/service-based business.

Promotional analysis is a technique of analyzing success or failure of a promotion using past time series data. 

Types of Promotion Analysis include:

  1. Quantity/Product concession
  2. Price discount
  3. Ads
  4. Shipping promotion

9. CONSUMER CHOICE MODEL:

The consumer choice model is used to determine the buying decisions for several commodities with different results. Such models take into consideration different families, classes, attitudes, etc.

The basic steps for Consumer Choice Model include:

  1. Recognition
  2. Information search
  3. Alternative evaluation
  4. Purchase decision
  5. Post-purchase behavior

10. AD OPTIMIZATION

Some ads do well than others and generate more clicks, revenue, conversions, higher conversion ratio, etc. Based on these features, you can display the better performing ads more and show a red flag to the poor ones to boost the ROI.

The Ad Optimization models operate by:

  • Adding a call-to-action model
  • Adding emotional content
  • Using Ad Extensions
  • Trying dynamic keyword insertion

11. PROPENSITY MODEL

The method of predicting the possibility that visitors, customers, or leads to conduct certain actions are termed as Propensity Analysis.

Based on Propensity modeling, marketing teams forecast the likelihood of whether a lead will convert to a customer, or will churn. Add to it, propensity modeling also helps to predict whether an email recipient will unsubscribe or not.

Here is how Propensity Modeling works:

  • Determining the features
  • Preparing the propensity model
  • Calculating your Propensity scores

12. TIME SERIES AND CASUAL ANALYSIS:

Time Series Analysis is used to clarify, track, and forecast casualty behaviors of customers. Whatever time-based patterns business experiences, Time Series Analysis can be used to determine it.

Time Series models can be applied to the following applications:

  • Business- Web traffic, Supply Chain, etc.
  • Finance- Stock option, econometrics, etc.

13.FRAUD MANAGEMENT AND PREVENTION:

Spotting potentially fraudulent behavior and identifying unusual patterns of behavior consistent is termed as Fraud Management and Prevention.

Fraud Prevention models provide:

  • Expert alert scenarios
  • Real-time integration
  • Quick roll-out
  • Audited workflow and case management
  • High performing testing tools

In an era of machines and Artificial Intelligence, the traditional form of banking has taken the back seat, when compared with tech-savvy Fintech players of the industry who are keen to adapt latest technologies to keep up.

As per market experts, it goes without saying that AI will empower Banking Services, redefining operations by innovating products and services beyond the conventional norm. This eventually perks up customer experiences. Leveraging advanced technologies, human workers will be replaced with sophisticated algorithms.

Such a competitive edge can only be achieved by banking corporations by embracing AI and weaving it into strategic business decisions for maximum profits. 

AI can double Business Profits

AI professionals quote; there are three primary ways the introduction of AI in the Banking Sector can shoot business profits.

Here they go:

  • Enhancing contemporary Profits and Loss levers
  • Identifying new growth patterns
  • Delivering the Digital bank

But the question is: How AI can achieve this objective? The answer to it is by:

  • Profiling and Customer Segmentation– Being aware of the financial profile of all the customers helps banks to jack up the expenditure and income for next month, and maximize revenue.
  • Customer Spending Pattern Analysis- Typically, banks have adequate data about a customer’s flow of income every month, net savings and utility expenses. With the help of this model, banks can conduct a risk assessment, loan screening, cross selling of financial products, and mortgage evaluation.
  • Evaluating Creditworthiness- Determining whether an individual is likely to be a defaulter and estimating the amount that can be offered to him/her.
  • Transaction Channel Identification- With the help of AI, banks can determine if a customer is likely to keep or withdraw money on payday. The latter customers can be pitched for short-term investments.

All the above-mentioned objectives have one thing in common- SPIN Strategy AI Models.

SPIN’s AI models are banking Operation-Focused

At SPIN, we provide a range of AI and ML learning tools for a host of operational applications for financial institutions. Such tools include:

1. Risk assessment, compliance & reporting 

Comprehends the spending pattern and analyzes previous credit history to assess the risk of issuing a loan to a customer.

2. Sentiment Analysis: 

Customer view and market rumors play a significant role in business, and with NLP it is easy to comprehend customer sentiment and its impact on the enterprise.

3. Propensity Modeling

Uses historical data to make business predictions by directing consumers to the right messages and website locations.

If that’s not all, here are a few factors to conclude the fact that SPIN  Models are a boon for the banking sector.

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SPIN for Banking Services: Here is why

  1. Communication: We keep our customers in the loop with necessary and crisp data, perfecting the art of delivering the right solution to the right clients at the right time.
  2. Minimal expenses: No fluctuating invoice rates for our clients. We maintain a simple flat monthly rate.
  3. Rate: Simple monthly pricing for your business requirements at easy rates.
  4. Experience:  Maintaining optimum performance standards, our experts use AI-models for Data Integration, Conversion Optimization, Data Analysis, etc.
  5. Data Integration: We combine data from multiple digital marketing platforms to prepare customized reports to generate a holistic understanding of your brand’s ROI and growth.
  6. Predict Churn: Using AI and ML, SPIN identifies segments of the customer base who are about to leave you for your competitor brand.

The result of using SPIN AI Models for Banking Decisions- Improved decision making for credit and loans.

Here is a real-life scenario- Case Study

The Client-  The client offers financial services and technical support in a bid to increase productivity, boost innovation, and add economic integration. 

The Issue– The bank officials have diverse knowledge on various topics, and have shaped themselves as SMEs of certain topics. They share their knowledge from different geographical locations. The client wanted a single integrated knowledge management platform for individuals to reach out to these SMEs for a certain project in hand.

The Solution- SPIN worked with the client to deploy a cognitive computing application that would include a virtual agent to comprehend Natural Language request inputs and utilizes cognitive deductions to answer the queries.

In a bid to simulate human-like conversations between the systems and the users, SPIN leveraged Advanced Text Analytics including entity extraction, keyword extraction, emotion analysis, sentiment analysis, etc. 

The Result- The bank now has the faith that productivity across the globe will hit the roof due to the easy availability of SMEs. This will boost operational efficiency by 30%-40% and expand cross-broader collaboration and employee management too.

To conclude with

We are more than happy to help Banking Organizations with AI and Data Analytics services. Guaranteeing a solution that caters to clients’ needs and budget, at SPIN we ensure to provide excellent value for businesses. 

For more info, visit: https://www.spinanalyticsandstrategy.com/

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In a competitive era, when every industry is trying to reach the pinnacle of success, the healthcare industry too is picking up the pace with enhanced and exceptional Patient Experience

Be it the hospitals, nursing homes, physician practices or healthcare systems, all are laying special emphasis on Patient Satisfaction. Period.

Only healthcare facilities equipped with improved functionality and better convenience, providing whatever modern consumers look forward to, are emerging as the true leaders in the healthcare domain. 

Since patients these days are far more particular than before and have ample options to choose from, it is imperative for healthcare services to be proactive in roping in and hanging on to patients.

And Sentiment Analysis is one of the best ways to do it. 

To start of with Sentiment Analysis

Have you ever wondered what the feedback of your patients is once they leave your medical facility? 

Enters, SPIN NuInsights Model, with its Sentiment Analysis Application that can gather authentic patient feedback and transform them into reports and intuitive charts to help you draw a conclusion where the medical services are lacking or pleasing.

In tune with it, examining trends for different human emotions like sadness, anger, joy, disappointment, etc. by NuInsights Sentiment Analysis Model enlightens a healthcare provider about the present status of patient care services and the areas of improvement.

To cut the long story short, we want to endow healthcare providers with the tool and cutting-edge analytics (known as Sentiment Analysis application) that can nourish and boost their relationship with patients.

So what is Patient Sentiment Analysis?

Simply put, the process of evaluating and analyzing feedback from the patient’s experience (typically based on emotions at the time) is termed as Sentiment Analysis.

Unraveling of opinions, emotions, and reactions of patients based on the practice they just experienced is one of the biggest Sentiment Analysis benefits.

Add to it, such a form of analysis empowers healthcare caregivers with a competitive edge, revamping their service based on the feedback shared.

Furthermore, SPIN’s Sentiment Analysis Application can also be used to observe the texts or comments by patients and their overall experience in the facility, shared on Twitter, Facebook,LinkedIn and other popular online platforms.

The Significance of Patient Sentiment Analysis for Healthcare firms

It enables caregivers to evaluate the genuine notion of patients for them, figure out the gaps in patient experience, and employ corrective measures on time.

Here are a few pointers to support the above statement:

1. Accumulating data on patient experience

Patient satisfaction is the key for healthcare organizations to prosper and Nu Insights’ Sentiment Analysis Application excels in it. 

It analyzes the data accumulated from multiple common complaints or compliments, segment the data based on doctors and other parameters to spot the opportunities for patient care improvement.

2  Measuring performance 

Such analysis takes into consideration the emotions of patients to derive a conclusion about the performance of a medical facility. 

Such analysis classifies patient comments into various categories like places, processes, people, etc., and then gives them scores to uncover what patients actually feel about the facility services.

For instance, Nu Insights Sentiment Analysis model analyses the frequency of words used by the patients and selects only the most used words left in the comments. 

Here are a few examples:

  • We waited in the queue for a long time (Negative feedback)
  • Good staff and less waiting time (Positive)
  • Nice services but certain aspects need to improve(Neutral)

Based on the above such comments from patients, the Nu Insights Model determines whether the healthcare practice has any scope for improvement or it is going downhill.

Benefits

  • Insights into the performance of a healthcare facility are one of the biggest benefits of Sentiment Analysis. The evaluation of collected data gives a clear picture of what patients actually expect from your practice, how are your employees seen, and other aspects of the management.
  • Generating authentic and quantifiable data regarding the staff about their performance motivates employees to do better in the future. In case of negative comments, the gaps can be fulfilled by the staff. The bottom line is, as long as employees are motivated and get proper feedback for their hard work, patient care is bound to improve.

How Nu Insights Uses ML for Sentiment Analysis

Nu Insights’ Sentiment Analysis model can give you a clear idea of what patients are thinking about your practice by gathering feedback from various locations and using ML to analyze and use the data to make charts and reports.

Here is an example, how our Nu Insights’ Sentiment Analysis model applies to healthcare business scenarios.

A leading hospital chain in the US witnessed declining patient satisfaction to a significant level and wanted to incorporate the latest ML and Analytics trends to comprehend what opinions patients had about the facility. 

In this regard, effectively using Advanced Analytics to understand the root cause of the issue and creating a mechanism to track the same in a bid to timely correct customer satisfaction was the primary approach of SPIN.

 Scenario

With multiple facilities operating under the hospital chain, proper tracking and standardization of its operation became quite challenging and difficult. With an alarming fall of patient satisfaction leading to higher attrition, management decided to get to the bottom of the issue and address it.

Challenges

  • Removing unstructured data from different platforms
  • Same comments on different platforms
  • Determining fake reviews

Solution

Patient comments and reviews shared across different platforms like social media, review forms, blogs hospital websites were accumulated and were filtered for clarity, authenticity, and relevance. Then these comments were subjected to NLP for further filtration.

Results

The gaps that required quick attention were spotted and quick changes were applied in the operations, leading to enhanced customer satisfaction. Not only best practices for business were determined and standardized, the weekly tracking of the facility operations was also streamlined.

To improve healthcare, Sentiment Analysis is needed

Patient satisfaction takes the front seat when it comes to measuring the achievement of your healthcare practice and is always the primary focal point of any marketing campaign.

For marketing campaigns to prosper and reap necessary results, physicians, health centers and healthcare facilities can use Sentiment Analysis to take their clinical services to the next level, by analyzing the exact expectation of patients.

At SPIN, our healthcare marketing strategies are integrated with programs that boost patient experience significantly.

After all, it is a satisfied patient eventually lead to an increase in revenue.

It is an inevitable fact and goes without saying that on account of the very nature of data, technology, and analytics that is always at odds for different enterprises, optimal Big Data Deployment strategy may sing a different tune for other business. Period!

This fact-based truth opens the door for strategies, customized only for particular organizations with the pre-conceived motive to deploy Big Data technologies minus any kind of fall out or interruption.

Sounds soothing to the ears?

Note: Before kicking off with the ‘deployment’ process, it comes as a de rigueur step to conduct a detailed evaluation of integration, governance, security, processes, and interoperability, in a bid to reap the pleasure of a seamless Big Data implementation.

Let’s dig deep and get to the pulse of new age Big Data architecture and its deployments know-how for a better understanding.

Age of Big Data Technology

Thanks to our good fortune, we live and breathe in an era of Big Data Analytics, where business enterprises go that extra mile to find ways to harness volumes of unstructured data efficiently.

Business organizations these days take the helping hand from analytics to convert data into valuable insights to pave the way for enhanced operations and informed business decisions.

Riding on the back of intelligent algorithms, organizations give birth to smart data, which can evaluate patterns and signals to help business leaders make informed decisions and thereby cut down costs to perk up profit margins.

However, such an objective is not a walk in the park and requires the assistance of requisite technology within Big Data Environments.

Taking the plunge in the absence of required knowledge and an infallible Big Data strategy is bound to witness a dead-end.

Imperfect deployment road-maps and wrong decisions which can drain out the resources and budget and adversely impact the business performance further gives clarity on the gravity of the situation.

So it is important to understand the Big Data Deployment framework for effective execution.

Big Data Deployment Framework

Out of the lot, there are certain primary factors that manipulate the mammoth decision of employing Big Data technology in a business enterprise.

Such factors include:

• The existence of traditional/non-traditional data in the system
• Presence of low latency data
• Delving into new analytics algorithms
• The requirement for real-time insights

Pillars of Big Data architecture- Analytics, Data, and Technology

Data
There can be no objection to the fact that ‘Data’ is the very heart of technology, analytics, and strategic decision making.

Based on the volume, shape, and latency the type of Big Data technology to be deployed in a company is determined.

Precise mapping of data properties, frequencies, and sources are tagged as significant angles while devising the development strategy.

Analytics
Analytics is quite likely to include cultivating and operationalizing predictive, descriptive, text mining leveraging data sources.

To make the most of analytics, an amalgamation of traditional technologies and a distributed environment for Big Data can possibly the best road open for some companies to accomplish their business objective.

Technology
For most business leaders, it is relatable when we quote that ‘the present infrastructure in many companies is limited to minor data problems’.

Close examination of the existing hardware and software can bridge the gap between tradition and modern approach with ace technologies and predominant systems.

Finally Big Data Integration

The whole idea behind the smooth integration of Big Data technologies in the present infrastructure is to achieve no disruption, zero business downtime, and no cost overruns.

For such integration, numerous databases, nodes, and clusters are required to be explored.

At a business enterprise level, cross-project inter-departmental and multi-platform integration of Big Data technologies must be decided at an early stage, since this can be a difficult task to complete later on.

Concluding Note

Like it or not, a comprehensive, rigorous and importunate decision-making is the absolute need of the hour for deploying Big Data Technologies in any company.

Furthermore, the strategy should make amends with the changing landscape of Big Data technologies.

The deployment strategy is more than a certain piece of information jotted down on a piece of paper.

At SPIN, we understand the mechanism to dispose of Big Data strategy within the present infrastructure of a company in order to bring to pass maximum impact (in a positive way).

Connect with us and believe us. Today!