Category: Big Data

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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!

The unremitting expansion of data with time has compelled businesses to place data at the very center of any strategic business decision to safeguard a bright lucrative future.

In a bid to stay ahead of the pack in the days to come, businesses are using Predictive Analytics to make use of unending growth opportunities.

Over time, Predictive Analytics has gained its glory, thanks to its multiple applications.

Time and again, the concept of Predictive Analytics (which is also called as Advanced Analytics) has been linked with one of the most popular trends-business intelligence.

Whether they are linked or not, is a different story altogether, but their motto is unanimous: provide benefit to the company and its clients.

So what is Predictive Analytics?

It refers to the effective amalgamation of ML, historical data along with AI to take the most feasible hunch about the business possibilities of the future.

The historical data collected is added in a mathematical model that gives assent to key patterns and trends in the data.

In the next step, the model is used for the current data to figure out what will happen in the future.

Here is the workflow of Predictive Analytics for a better understanding.

Access and explore data- Process the data-Develop Predictive models-Merge analytics with systems

Prescriptive Analytics – commonly used terms with Predictive Analytics

Companies that have effectively integrated Predictive Analytics tag Prescriptive Analytics as the next lucrative frontier to approach.

Questions may arise as to how both the terms are inter-linked.

The answer to this lies in the fact that while Predictive Analytics gives a vivid picture of the possible future.

While Prescriptive Analytics reels off how to respond in the best way possible, in tune with the prediction.

How Predictive Analytics exercises its duty?

  1. The first and foremost step of Predictive Analytics is to figure out what are the questions you wish to be answered, based on the past data.
  2. The second step includes figuring out if you have the right data to answer the questions you asked.
  3. The third step includes training your business system to learn everything from your data to forecast outcomes.
  4. Plan your modules
  5. Use your forecasts and insights in your line of business applications for priceless outcomes.

Does your business need Predictive Analytics?

Irrespective of the fact that there are numerous aspects in a business, that needs special attention, Predictive Analytics finds its fit in almost every bit of an organization.

Here are few pointers to start-off with:

  • Customer Relationship Management (CRM) – Predictive Analytics models can be applied to enterprise applications like CRM, to figure out proper messages to target the customers in the days to come. By predicting the next likely move of the customer, you can spend your messaging dollars effectively.
  • Marketing- Using Predictive Analytics it is possible to determine the preferences of the customers based on past data and previous history. This will help to predict the future course of action for the company to retain more customers and increase productivity tenfold.
  • Manage risks- Using Predictive Analytics effectively can help businesses to sketch a roadmap for the company. By predicting future outcomes and possibilities, Predictive Analytics can help organizations to cut down risks significantly.

Let’s start predicting

Companies that have deployed Predictive Analytics in their business operations have flourished beyond expectations, in comparison to the ones who still playing with the thought of it.

Comprehending customers better by tapping on their requirements, and customizing the content as per the needs does wonder for a business, and Predictive Analytics is the key to it.

Well-informed use of Predictive Analytics helps organizations to be aware of market forces and secure their dominance in today’s competitive world.

All in all, predicting the future outcomes guarantees one important fact- substantial gain for the business and its clients.

Still, think Predictive Analytics is a bit confusing for you? SPIN is here to clear all your doubts.

 

Cyber attack! That’s the term that has possibly become a nightmare for businesses these days.

Time to control such exposed threats has stepped in. With a joint effort of technology and Cyber Security Programs, the avalanche of data making its way towards you can be managed.

Cyber attacks are prevalent and spare not a single sector that has an online presence.

The upsurge in dependency on networks, information, control systems. And the surge in the technological and organizational finesse of hackers boils down to only one thing- the higher risk of cyber attackers.

The number of sophisticated cyber attacks is rising. And the thriving role of vicious insiders in recent large-scale security breaches clearly indicate the need for data breach response plan.

Point to note- such a plan should offer more than a traditional approach and should actually keep up.

Time has changed

Change is prevalent, and cyber security concepts are no exception.

With time, the focus from safeguarding physical assets like stock or offices has shifted. Shifted to technology and software systems.
The primary motive behind this paradigm shift is to shield and go to bat for a business digital property.

Riding along with this transformational wave, companies have understood the need to re-strategize their cybersecurity. Analytics has emerged as the main bargaining chip when it comes to cyber resilience. All thanks to the persistent and highly advanced attacks.

And thanks to the adoption of Analytics, PDR (Prevent-Detect-Respond) has come to the picture. The below-mentioned bullet points will give a brief overview. This overview entails how cybersecurity analytics will bring the ball in your court.
• Detects infections, attacks, intrusions in seconds
• Transforms record of activity and volumes of raw unstructured data in meaningful actionable insights
• Gives an appropriate decision about the breach, its probable impact, and action to be taken.
• Instant response to detecting the infection, prevents data loss and averts outward intrusion.

How Big Data Analytics can help combat cyber data breach?

It is truly ironic that it is data, which is getting poached or stolen and it is only data that can put an end to such business ending breaches. All a business needs to do is to be able to use data in the right manner, and this process is called Big Data Analytics.

Here is a detailed summary of how such analytics can be the right fuel for cybersecurity programs. It can change the game for your business, and you can bid ‘TATA’ to online threats almost forever.

• Detect abnormality in device behavior
Off late, in many cases, it has been noticed that employee device is often used as the tool or podium to implant a Trojan horse.  And eventually, lay hands on important data of the company.

The good news is, it can be stopped by incorporating Big Data into the system.

• Identify anomalies in the network
With the help of Big Data, it is possible to find out the new lurking threats. If that’s not enough, Analytics correlates with the data available. This helps to draw a conclusion about the very nature of the attack.

• Analyses and identifies the network vulnerabilities
One of the most commendable approaches of Big Data Analytics is to devour the data. After that examine it and figure out which database has the customer identifying information and how prone it is to prevailing risks. Big data also shut the door on any potential source of risk for the online presence of a company.

Big Data Operationalization Benefits

Hard to admit, but it is true that identifying potential risks is not going to shun away the peril in question. It is imperative to derive the true value of Big Data insights to drive the required actions with relevant departments.

Being armed with operationalization capabilities, which can shift the data, locate the right signals and then drive the right action, is the need of the hour.

In tune with this, recently Oracle has waged a war against cyber threats with its Gen 2 Autonomous Cloud Infrastructure in a bid to ignite the fire of battling with cyber demons.

If you too are a victim of Cyber Threats and is convinced that only Big Data Analytics can come to your rescue, contact SPIN for your next move and provide the life-long security your organization deserves.