Many small scale industries used to ask whether big data can offer the same advantages as it offers big corporates. Big data analytics for small business is a big plus. While most of the small scale business has less self-generated data than big companies, that doesn’t mean big data can’t offer much. In fact, in various ways, big data is more suited to small businesses because they are more agile and can act more quickly & efficiently on data-driven insights.

1. Big Data Analytics For Small Business Grants
2. Big Data Analytics For Small Business Development
3. Big Data Analytics For Small Business Solutions
4. Big Data Analytics For Small Businesses
5. Big Data Analytics For Small Business Organization

No one can ignore the importance of small business to any country’s economy. In the US, almost 28 million businesses are into small scale industry and more than 50% of the working population (120 million individuals) works in small scale companies. Before the evolution of digital media, & when traditional marketing methods were at their peak, it was difficult to track sales & conversions. Even it was difficult to measure how many people saw your ad or open your email. The internet has changed it all. Now a business can see page views & opened emails. Businesses collect thousands of data on an everyday basis. Big data helps in analysing these data to make smarter business decisions. It is all about making sense of the unused data and uses them in the right place.

A lot of small business owners take difficult business decisions based on market trends, past year performance and even at times based on intuition. But what if you could tell exactly which day of the week has highest email open rates or maximum page views or website traffic. Which ad of yours has the highest reach or which is the medium bringing more traffic to your business page.

1. So, if you have a small business and looking for a Good Big Data analytics, then we have the solution for you. ClearStory Data. ClearStoryData helps in making decisions not just based on your data, but also the data which is publicly available. It also provides many features through which you can create interactive visuals, story lines,.
2. For SMB, Big Data means better business decisions. With Big Data solutions like Google Analytics, a free web analytic tool that provides companies with data about website traffic, your business can ascertain: What people are searching for on your site How many people are visiting your site.

All this is valuable business information. Many times, they are driven by intuition or past trends but it doesn’t have to be that way. Several businesses – large or small – are using big data to improve their product quality, make their marketing efforts more relevant and build stronger relationships with their customers.

If you are running a small business, the fear of big giants is always there. Latest technological trends can be a real threat to the sustainability of your business. But what if you could turn the tables on them and use technology to change the business landscape. Many small businesses believe that big-data analytics projects are too costly and complex for their business model. Most of the analytics solutions in the market were developed especially for larger enterprises. Solution providers won’t understand the requirement of small scale industries as they are targeting big corporates. But, fortunately, with big data now spreading its wing to transform businesses, new and customized solutions are coming to the market on a regular basis especially for smaller businesses.

As a small business, you may think you don’t have enough data to extract and utilize for your business strategies and goals. But, it’s not just the big corporations that can turn past and present data to predict and create better operations and marketing strategies to increase profitability.

Now, how small businesses can use big data in a bigger way?

#Understand your target audience

Small businesses can understand their customers – what makes them happy, buying habits, purchase trends, why they switch, what are the factors lead them to refer a company to others, Thanks to big data. Small enterprises are interacting and engaging more with customers by analysing trends and feedback in order to improve their service. Data which includes sales data, customer service logs, product reviews, customer feedbacks, social media performance are the key in measuring the market performance and future opportunities.

Social media has evolved as a valuable source to collect raw data, which helps to identify your niche and actual buyers.

#Trend Identification

Identifying and understanding consumer behaviours and trends allows businesses to predict where their business is moving, which product in your product line has the highest demand, and which product is not performing. Big data is slowly taking the place of gut instinct and giving you the exact numbers and reports.

In social media, you can see trending topics every day, which makes it easier for businesses to work out what customers want. Various industries are analysing business trends related to their industry by using data and take strategic business decisions more effectively than ever before. Retail, healthcare, hospitality, travel, online shopping are few of the industries who do their business heavily depending on the market trend. Small businesses can compare their business data with external data, such as economic condition, weather condition, national budgets, FDIs etc. to build up a detailed picture of what people are likely to buy and when.

#Business Information Analysis

Every small business face one common challenge which is to understand what sort of information is most relevant to their type of business. Sometimes less is more. The best way to start is to prepare a set of a questionnaire of critical information most relevant to your business. Ideally, you should identify that information which can increase the bottom line of your business and service offerings. Which means data which can help you increase revenue and cut down operational cost.

Let’s take the example of a shopping site. Information related to purchase pattern is more relevant than anything. Percentage of men purchasers, women purchasers, time of purchase, most visited products, mode of payment etc. are relevant. After collecting data related to this, you can customise your offerings or coupon codes to increase sales.

Similarly, if you are into travel & tourism industry you need to understand best time of the year to travel, most favourite places, best staying places, most visited locations, age group, gender etc. to offer best possible service.

Small businesses have the same amount of access to big data as large enterprises. There is no reason why big data cannot be leveraged for small business.

– John Smith, CEO of Remote DBA

#Technological Requirement

Use of technology at the right place could be the best possible business strategy. You can get rid of many tough business challenges with the effective use of technology. While analysing technology needs, it’s important to analyse its flexibility and simplicity. Sometimes, technology can provide you with a lot of information but it’s difficult to use and businesses end up not using any of the information.

Big data analytics is the best available technology which can help your business achieve a technological edge over your competitors and obtain the highest level of customer satisfaction. Tools are there from managing business operations to analysing market trends and buying habits. Identify which tools are going to help your business model and take the advantage of technology.

#Competitor Analysis

Previously, understanding your competition was limited to looking at their website and sometimes pretending to be a customer in order to find out more about their approach and service. But, now you don’t even need to go out to check what your competitors are doing. Financial data is there, google trends are there to offer insights, and social media analytics can give you the exact numbers. Twitter, Facebook will give you an idea of what customers are thinking of them. After collecting all these information you can analyse and compare it with your own business model and offerings. If your competitors are having more mentions on Twitter or have more followers on Facebook, then you need to work hard on your service offerings and business model.

Even vice versa can happen. Your competitors can easily go through your business data and formulate their business plan. There is no other way around this, but what you can do is to stay one step ahead by keeping all the data and use it effectively at the right time by the use of big data tools and technologies.

#Tracking Results

Every business can be benefited from analytics. This is where big data comes into play. It allows small businesses to track the outcomes of their online campaigns, promotional strategies, market research and customer relationship management. This helps them to improve their decisions for better results in future. Small businesses can use data to understand how their brand is being perceived by customers. Based on that, businesses can make future predictions about their products to minimize business risks. 72% of the SMEs believes that they primarily use big data to improve business operations and track results.

#Decision Making

Big corporates may have more money and manpower, but they lack the speed and agility to respond quickly and take decisions instantly. Real-time changes and decision making is something SMEs can excel at. A big business needs time to implement any business strategies as it needs many approvals as well as reviews, while small businesses can afford taking risks and quick business decisions. They can collaborate data with intuition. Big data lends insight into what is trending now while making future predictions.

If you want to sustain in the market, you need to master the ability to implement quick changes. When something is trending, it’s your time to grab the opportunity and capitalize on that. Big data helps modestly-sized companies ride the wave more.

Flexibility in taking business decisions is also important when responding to business challenges. Rather than letting customers slip away, taking cues from negative data and responses is key to cutting back on churn. Even sometimes small complaints paint a big picture.

#Optimizing Business Operations

Most of the companies use big data to optimize their business operations. Any business which generates data, big data can be used to make improvements to enhance efficiency. The overall business operation can be optimized and which results in a faster process and cost reduction.

Retail companies can optimize their warehouse operations by the use of big data technology. They can keep their stock as per the data reports after analysing which products have more demand. Stocks can be maintained based on predictions generated from social media data, web search history as well as ATL/BTL campaigns.

Logistics industries optimize their supply chain process and delivery route optimization with the help of big data analytics. GPS and sensors are used to track goods and vehicles to optimize routes by integrating live traffic information and shortest distance as well as maximum space utilization.

#Talent Management

Data can help small businesses to hire the best talents available in the market. Data analytics helps to identify the best recruitment channels, and also helps to engage the current workforce in a better way. Most small businesses already generated a gamut of HR related data: candidate database, profiles, portfolios, reviews etc. Companies can now access so much data that wasn’t available before: from recruitment sites, information from sensors, social media data, internal reports etc. All this information needs to be analysed to gain insights that were never available before for use.

#Revenue Generation

Big data allows small businesses to a healthy number of business opportunities. And the most important thing is that without any upfront cost. Data sources available to small businesses are free of cost. Social media interactions can be the best example. You can easily analyse the page views, conversions, traffic etc. at zero cost. Google analytics can be used as an effective tool to identify consumer behaviour on your business page. Based on the resources, you can provide customized solutions to attract a number of customers. You can also target a specific group of buyers, which can open up ways for revenue opportunities.

Our Take

Big data has got all the potential to change how a business operates. So, adopting a big data strategy will certainly going to change the way small business works. The first step is very vital. Don’t expect too big. Use data analytics for the right process. Make your goal very clear. Data can be collected from a vast array of sources, but if a small business identifies the right source, big data can do magic.

It is better to start from understanding the importance of data that a small business generates. No requirement to install any sensors to capture data to analyse. Analyse the data you have from social media, website to start with. Mining that information for helpful insights is a good first step before entering a big data strategy. Small business owns should specify their goal and result they want to achieve. Without clear direction, companies will spend a lot of time in collecting, and analysing data with no real use. With a clearer picture and goal, small businesses can proceed with confidence and can play with data.

Visionary business leaders who think about future, uses big data for a competitive advantage. They use big data concept both in content & format. If you want to grow, you have to be adaptable as well as do experiments. Only Innovation can make you a market leader. There are few key takeaways from this blog;

• There are more spending in big data than any other technology.
• Before implementing big data analytics you should be aware of industry specific challenges, data characteristics of your industry, need to spend at the right place, need to match market expectations with your own capabilities.
• Industry expertise is most important for using big data effectively.

All you need is a strong mind set to start something. It’s never too late to initiate something new. Apogaeis is a technology service provider & has the expertise to help small businesses in identifying & fulfilling all technical needs. Our data analytics consultants are well versed in their domain and we are the first choice technology partner in big data analytics for small business. Want to know more, we are just a click away. Connect Here A first round consultation is FREE.

Big Data — is a pretty common concept in IT and digital marketing. Essentially, the definition is on the surface: the term “big data” implies managing and an analysis of big volumes of data. Broadly speaking, this is information which cannot be processed by classical approaches due to its volume.

Big Data — what it is?

Big Data Analytics For Small Business Grants

Digital technologies now appear in every sphere of human lives. Data volumes recorded in the worldwide storage increase every single moment, and this means that information storage conditions should modify with the same page as well as there have to be new volume buildup possibilities.

IT experts suggest that the expansion of Big Data and its acceleration of growth have become the objective reality. Every single moment giant volumes of content are being generated by such sources as social networks, information websites, and file sharing platforms which altogether only represent a hundredth of suppliers.

According to the research conducted by IDC Digital Universe, in the nearest 5 years the global data volume will roll over to 40 zettabytes, so that by the year 2020, there will be 5200 gigabytes of information per capita.

In fact, general information streams are not being generated by people. It’s constant interaction of robots that serves as a source of data generation. These robots are monitoring devices, sensors, surveillance systems, operating systems of personal devices, smartphones, intelligence systems, transducers, etc. All of them set the frantic growth rates of data volumes which lead to the necessity of growing the number of working servers (both live and virtual) and, as a consequence, of expanding the existing data centers and creating new ones.

Fundamentally, Big Data is a pretty relative and contingent concept. The most common definition is a set of information superior to the storage capacity of one personal device which cannot be processed by classical approaches used in the processing of smaller data volumes.

What is the Big Data technology? Generally speaking, the processing technology of big data volumes can be narrowed down to three major courses solving three types of tasks:

• Storage and transferring of the incoming information to gigabytes, terabytes and zettabytes for holding, processing and practical application.
• Structuring of the scattered content: texts, images, video and audio files, and any other types of files.
• Big Data analysis and the implementation of various approaches to processing of the scattered information and analytical reports generation.

As a matter of fact, application of Big Data implies all aspects of working with big volumes of the scattered data being constantly updated and diffused to different sources. The objective is clear – maximum operational efficiency, introduction of new products and growth of competitiveness.

Big Data issues:

Big Data issues can be narrowed down to three “V” categories — Volume, Velocity и Variety.

Storage of big data volumes requires special conditions, which are the question of space and capabilities. Speed is not only connected with possible slowdown and stopping caused by old processing methods, it is also a matter of interactivity: the quicker the process is the bigger output is and the more productive result is.

Dissimilarity and scrappiness issues occur due to an inconsistency of sources, formats and quality. In order to joint data and process it effectively, you not only need to bring it into a suitable shape, but you also need particular analysis tools (systems).

It is only part of the story. There is an issue of data value limit. It is difficult to set, and it means that you cannot predict what technologies and investments you need for further development. However, in case of specific data volumes (for instance, a terabyte) one can use the existing procession tools which are growing rapidly.

There is an issue connected with the absence of transparent principles of working with such a volume of data. Dissimilarity of streams only makes matters worse. How to appeal to their application to make them work? The development of new Big Data analysis tools is needed here to make a stream helpful information provider. According to suggestions of the U.S. universities representatives, now is perhaps the time to introduce and develop the new branch of knowledge – the study of Big Data.

Basically, this is the reason of delay of the implementation of Big Data projects in companies (putting one more factor to the side – relatively high implementation costs).

Selection of data for future processing and the analysis algorithm may cause troubles because there is imperceptions of what data should be gathered and stored and what data can be neglected. Another pain point falls into place – lack of professional specialists who can be trusted to conduct an in-depth analysis, to generate reports for solving business tasks and consequently to extract profits (return investments) from using Big Data.

Another Big Data issue is ethical in nature, namely: how does data gathering (behind user’s back) differ from violation of the privacy right? Thus, the information saved in search engines such as Google and Yandex lets companies update their services, make them more convenient for users and create new interactive programs.

Search engines record every user click on the Internet; their IP address is open along with location, interests, online purchases, private data, messages in their inbox and other factors which allow demonstration of contextual advertising according to user behavior on the Internet. In addition, companies do not ask for permission of such actions, therefore a user cannot choose what information they will provide on themselves. In other words, in Big Data all information is being gathered by default and then stored on servers of those websites.

Here we come across another issue – data security and safety in use. For instance, the information on potential customers and their website traffic in online stores can be used in solving various business tasks. But safety of the analytical platform to which users automatically transmit data (simply because they entered a website) stirs up disputes. Even super protected servers of governmental secretive agencies cannot withstand current virus and hacker attacks.

Big Data history:

Big Data algorithms themselves appeared with the implementation of the first highly productive servers (mainframes) having enough capacity for quick information processing and suitable for computer calculation and further analysis.

The term Big Data has been first announced in 2008 on pages of the special issue of Nature magazine in the article by Clifford Lynch, editor-in-chief. That issue was dedicated to global data expansion and its scientific assignment.

Specialists maintain that any information streams with daily traffic of over 100 gigabytes can be called big data.

However, in the last couple of years scientists have been noting that the term Big Data became popularized much, it is now being used practically everywhere data streams are concerned, and, as a consequence, the term is now being perceived in too general and indistinct way. It’s incognizant journalists and inexperienced entrepreneurs using the term excessively who are to blame. From the perspective of foreign experts, the term has brought discredit on itself lately and now is the time to abandon it.

Currently world community is again speaking about big data. The reasons for this are constantly growing data volumes and lack of structure of that information. Entrepreneurs and scientists are concerned about qualitative data interpretation issues, development of tools to work with data and development of storage technologies. The implementation and active use of cloud storage and calculation models work towards the above mentioned kinds of development.

Big Data in marketing:

Information is a major aspect of successful growth forecasts and plotting of a marketing strategy capable hands of a marketer. Big data analysis method is for a long time being applied to determine target audience, their interests, demand, and user activity. Thus, Big Data is the most accurate marketing instrument for predicting the future of a company.

For instance, analysis of big data allows showing an advert (on the basis of the famous model of Real Time Bidding auction) to only those customers interested in the product/service.

Big Data application in marketing lets business people:

• Better know their customers and attract corresponding audience on the internet;
• Evaluate the satisfaction level of the customers;
• Understand whether the offered service meets customer needs and expectations;
• Find and implement new instruments increasing customers’ trust and loyalty;
• Create desirable projects.

Google.Trends, for instance, will accurately provide a marketer with a forecast of seasonal changes of the demand for a particular product, click fluctuation and geography. You can march this information to the statistics of your website and then lay out the plan of advertising budget stating the month and region.

20 BIG DATA MYTHS:

1. Big Data is something new

Actually data volumes have increased much lately. So did instruments and technologies which allow working with data. But you cannot call this process a revolution; this is rather a classic example of evolution. True, this evolution is exponential.

2. Big Data will change anything in the world

The biggest overheated myth caused by confusion in terminology. It’s confusion which lead to Gartner’s attempts to switch the focus from Big Data to Machine Learning. Numerous expectations have not been met, because Big Data does not always result in tangible benefits.

3. Primary Big Data costs are equipment and software

Unfortunately, in everything connected with the application of information technologies success depends on the team and specific members. As soon as the team masters the storage technology and derives benefits from the data, there is a new set of tasks connected with scaling, data security and management, testing framework and development process arrangements, staff training, etc.

4. You do not have to overthink about optimization when working with Big Data

If you have much data and the existing instruments allow analyzing it, this does not mean that you do not have to prepare the data. If you have bad data, then you will for sure have bad results. Working with big data requires the correctly arranged process, the most important stage of which is the preparation of data for analysis.

5. It suffice to hire one cool Data Scientist

You may start from one “universal soldier”, but you cannot do without a team. The best result is delivered when the analyst in on firm ground in terms of understanding the needs of a business. However, it is impossible to find specialists who have expertise in several business domains.

6. You necessarily have to implement Machine Learning

Statistically, 85% of what people consider Machine Learning relates to tasks of Statistics. Find Statistics specialists as a first step.

7. Every current problem is an issue of Big Data

For good or for ill, most tasks require small data. Big Data is not a universal remedy and cannot always provide the expected result.

8. We have to little data for Big Data

Many people think that they do not have enough data to analyze it with Big Data instruments. However, experience has proven that not all data in companies is being gathered, and some people are not ready to enrich their data at the expense of external data. Data increase every day. If you think today that you do not have enough data, when you look closer you will notice that today your data is too much.

9. We need real-time data

To build forecast models of high quality, you need historical data. That is why you need to first solve the task of data accumulation and then go to real-time analytics.

10. Data Analysts are “new gods” of the media age

They are not gods, but you cannot do without them. Nevertheless, instruments for independent working with data become friendlier. Marketers and business users currently can solve analysis tasks without hiring a Data Scientist.

11. Big Data knows answers to all questions

To get the correct answer you need to pose the right question.

12. Hadoop is the Holy Grail of Big Data

For effective work with Hadoop you need qualified engineers. And, for the right organization of work you will need numerous instruments and add-ons. Hadoop does not solve all your tasks.

13. Big Data is the problem of IT guys

If Big Data do not cross the line of IT department, then you can forget about any business breakthroughs. You can only enter upon the search for precious knowledge by involving business divisions.

14. When we have much data we can neglect errors in data sets

The issue of data quality quickly becomes the important task in an organization. Test and training samples of low quality may destroy all attempts of proving the correct hypotheses.

15. Classic data storages live their lives

Yes and no. The task of storage is to prepare qualitative data for further analysis. Within borders of an organization you need to understand how storage and “new big data” should coexist and be used in the general analysis and decision making processes.

16. Big Data are only for Big Companies

Today even small startups are capable of generating and processing huge bodies of data. In digital economy each business is based on data.

17. All our rivals have already implemented Big Data

Experience has proven that very few companies learned to work with their own data, and there are the very few companies that learned to cream off the best data. The early bird catches the worm.

18. Big Data excludes a human from a decision making process

Currently data analysis helps a person make decisions. Most business decisions are being taken on the basis of intuition and expertise, in which analysis results may only be an additive.

19. Users want flexibility, not recommendations

Quite the opposite. You do not need many options, much data, various metrics… All you need are pieces of advice and recommendations, and strictly limited number of options, from which you need to choose the most relevant option.

20. Nobody in our company is concerned with Big Data

Even if you do not hear anything about this, it does not mean that nobody discusses Big Data. Business needs more quality reports, instruments for effective decision-making, more quality customer segments, recommendation engines, etc. Nowadays making these decisions is impossible without analysis of various kinds of data – big, small, internal, and external.

Top 20 Big Data Analytics Companies For Small Business:

Content

IBM

IBM big data solutions can capture, manage and analyze huge volumes of structured and unstructured data to improve business insights. Read analyst report.

HP

Big Data and Analytics solutions from HP bring meaning, extended value and security to your data. Get everything you need from HP to profit from Big Data.

HP

EMC

Read how EMC’s scientists are using big data for their customers and how it becomes their most valuable asset.

BIGDATA.TERADATA

Big Data, Big Data Beyond the Hype and Big Data Successes | Teradata. Read about the difference btw Big Data hype & today’s proven Big Data successes from the leader in Big Data Analytics & data warehousing.

BIGDATA.TERADATA

ORACLE

What is Big Data? Learn how Oracle Big Data technologies deliver a competitive strategy on a unified architecture to solve the toughest data challenges.

SAP

Explore the features, capabilities, and benefits of the SAP HANA in-memory database and computing platform.

GO.SAP

ENTERPRISE.MICROSOFT

IT Leader Archives – English (en-us).

VMWARE

Big Data Extensions: resource efficiency and architectural flexibility. Easily deploy and manage an efficient and scalable Hadoop platform.

VMWARE

CLOUD.GOOGLE

BigQuery – Large-Scale Data Analytics — Google Cloud Platform. A fast, economical and fully managed data warehouse for large-scale data analytics.

SPLUNK

Big data analytics solutions: machine data can reveal customer behavior, security threats | Splunk. Splunk Enterprise is the leading platform to collect, analyze and deliver real-time insights from machine-generated big data. Try Splunk Enterprise and Hunk| Splunk Analytics for Hadoop for free.

SPLUNK

MEMSQL

MemSQL: The Fastest In-Memory Database. MemSQL is a distributed In-Memory Database that lets you process transactions and run analytics in real-time, using SQL. Download now and see how it works.

PALANTIR

Palantir builds software that connects data, technologies, humans and environments.

PALANTIR

TRIFACTA

Big Data Analytics For Small Business Development

Data Wrangling & Exploratory Analysis Platform. Trifacta’s self-service data preparation platform helps data scientists and analysts discover, wrangle, and visualize complex data quickly and intuitively.

DATAMEER

Datameer is the only end-to-end big data analytics platform for Hadoop that empowers business users to directly integrate, analyze, and visualize any data.

DATAMEER

TAMR

Tamr quickly, efficiently and cost-effectively connects and enriches all of your internal or external data sources—whether structured, semi-structured or unstructured—enabling you to leverage 100% of your data to drive innovation and decision making.

NEO4J

The World’s Leading Graph Database. Unlock the value of your connected data and build intelligent applications at scale with Neo4j, the world’s fastest and most scalable graph database.

NEO4J

Big Data Analytics For Small Business Solutions

DATASTAX

DataStax powers the big data applications that transform business and profoundly improve customer experiences through Apache Cassandra™, the massively scalable.

INFOBRIGHT

Infobright – Analytic Database for the Internet of Things. Infobright technology combines a column-oriented database with our Knowledge Grid architecture to deliver the ideal solution for your growing analytic needs.

INFOBRIGHT

FRACTALANALYTICS

Leading companies leverage Big Data, analytics and technology to drive smarter, faster and more accurate decisions in every aspect of their business.We serve as strategic partner to our clients where.

METRICINSIGHTS

Business Intelligence Software | Metric Insights.

METRICINSIGHTS

INFORMATICA

Informatica: Data integration leader for Big Data & Cloud Analytics.

SYNTASA

SYNTASA extends existing digital marketing analytics platform with a real time and highly scalable big data solution. Open source technology that integrates and analyzes both online and offline data. This reveals deeper, targeted customer insights that go beyond descriptive analytics to predictive and prescriptive analytics that allow advanced decision making.

SYNTASA

CHARTIO

Cloud Business Intelligence | Chartio. Know your business, grow your business. Chartio empowers the entire company to understand its data through powerful analysis and easy to create dashboards.

THOUGHTWORKS

Agile Development and Experience Design | ThoughtWorks. A global software company focused on software design and delivery. We provide professional services and products and leading thought on Agile and Continuous Delivery.

THOUGHTWORKS

PLATFORA

Big Data Discovery | Big Data Analytics | Platfora. Platfora’s Big Data Discovery and Analytics platform is the only end-to-end solution native on Hadoop + Spark.

CRAY

The Supercomputer Company | Cray. Cray, the supercomputer company, offers a comprehensive portfolio of computing, storage and analytics solutions designed to answer the world’s toughest questions. We’ve harnessed decades of know-how in our data analytics and data discovery solutions.

CRAY

SISENSE

Business Intelligence (BI) Software | Sisense. Business Intelligence software by Sisense, the industry leader in BI for complex data – easily prepare, analyze & explore growing data from multiple sources.

ZETTASET

Zettaset: Big Data Security, Big Data Solutions & Platform. Zettaset: a leading big data company offering big data solutions & big data platform designed to address enterprise requirements for security.

ZETTASET

CLEARSTORYDATA

The New World of Data Intelligence | ClearStory Data.

Big Data Analytics For Small Businesses

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Personal contact info – slikgepotenuz@gmail.com

Big Data Analytics For Small Business Organization

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CEO and co-founder at Cloudsmallbusinessservice.com