YOUR ORGANIZATION SUCCEEDS WHEN WE DO YOUR DATA.
Inexpensive and Affordable
Outsource/Out house Data Engineering – Data Science for your Data-driven Decision Making. We do your most cost-effective Data Analytics for your business decisions. Data is most important and has evolved. We do the “Big Data” work for you to succeed! Yes, we are in Africa and we do it at much lower costs to you!
Data-driven decision-making
Instead of going with a strategy you think is best, go with the strategy you know is best.Data-driven decision-making is a strategy that uses data to inform business decisions.
Often referred to as DDDM or information-based decision-making, you group together historical information to analyze trends and make decisions for the future based on what’s worked in the past – rather than make decisions based on gut feelings, opinion or experience.Companies that embrace DDDM position data at the core of every decision they make. Data-driven decisions make you less vulnerable to risky decisions going wrong.
Put simply, with us you do more of what works and or worked and less of what may or may not work – all based on the data you have collected to make smarter business decisions.
OUR DATA SERVICES FOR YOU
The world we live in today is driven by insights and decision making based by data and analytics. Information and technologies are so ubiquitous that your organization need not to be a digital titan (such as Amazon or Google) to excel and win in today’s world. Organizations and companies, large and small, now recognize the need to utilize the full value of their enterprise data to obtain an essential competitive edge. Since business competition can be fierce, it is now, more than ever, necessary for yourorganization/company to be able to adeptly appropriate data assets from operational, analytic, cloud, and big data systems.
Data Engineering is the catalyst and Analytics Applications can be the accelerant that fuel the fires of decision making that are at the forefront of today’s business world by helping users identify business problems and formulate data-driven solutions.
It is not enough to merely have a Data Engineer or an Analytics Professional on payroll—they must be part of an end to end program of bringing Data Discover, Data engineering, Data Science, then Data Consumption together. And even before taking the first step, it is necessary to “start with why” as in to identify the business’ pain point in terms of a well-defined need for a specific analytic insight. This identified pain point informs scoping of required data and development of a purpose-built analytic micro-application, providing the user with a complete data analytics model that offers data-driven solutions to address specific problems. So, let us help you get this done.
Mining data from various databases in order to generate new information and insights must be a precise, focused, and well-executed endeavor. Don’t waste time and get left behind by going data fishing. Efficient and effective Data Engineering makes utilizing data easy by providing the data discovery and integration layer within a modern enterprise data management stack, a data fabric. The layer provides companies a connected and business-oriented map that is customized to gather enterprise data. People across the business with data or analytic needs can use that map to explore, understand, connect and blend data into analytic ready datasets that combine any data from any system across the enterprise.
What is innovation and why do we need? want? it
Innovation, by definition, is the introduction of something new. Without innovation, there isn’t anything new, and without anything new, there will be no progress. If an organization isn’t making any progress, it simply cannot stay relevant in the competitive market.
Because organizations are often working with other individual organizations, it can sometimes be challenging to understand the impacts of innovation on our society at large. There is, however, a lot more to innovation than just firms looking to achieve competitive advantage.
Innovation refers to creating more effective processes, products, and ideas. For a business, it could mean implementing new ideas, improving services or creating dynamic products. It can act as a catalyst that can make your business grow and can help you adapt in the marketplace.
By innovation, we also mean changing your business model and making changes in the existing environment to deliver better products or services. Successful innovation should be a part of your business strategy, where you can create a culture of innovation and make a way for creative thinking. It can also increase the likelihood of your business succeeding and can create more efficient processes that can result in better productivity and performance.
Disruptive Innovation
Disruptive innovation, also known as stealth innovation, involves applying new technology or processes to your company’s current market. It is stealthy in nature since newer tech will often be inferior to existing market technology. This newer technology is often more expensive, has fewer features, is harder to use, and is not as aesthetically pleasing. It is only after a few iterations that the newer tech surpasses the old and disrupts all existing companies. By then, it might be too late for the established companies to quickly compete with the newer technology.
There are quite a few examples of disruptive innovation, one of the more prominent being Apple’s iPhone disruption of the mobile phone market. Prior to the iPhone, most popular phones relied on buttons, keypads or scroll wheels for user input. The iPhone was the result of a technological movement that was years in making, mostly iterated by Palm Treo phones and personal digital assistants (PDAs). Frequently you will find that it is not the first mover who ends up disrupting the existing market. In order to disrupt the mobile phone market, Apple had to cobble together an amazing touch screen that had a simple to use interface, and provide users access to a large assortment of built-in and third-party mobile applications.
Architectural Innovation
Architectural innovation is simply taking the lessons, skills and overall technology and applying them within a different market. This innovation is amazing at increasing new customers as long as the new market is receptive. Most of the time, the risk involved in architectural innovation is low due to the reliance and reintroduction of proven technology. Though most of the time it requires tweaking to match the requirements of the new market.
In 1966, NASA’s Ames Research Center attempted to improve the safety of aircraft cushions. They succeeded by creating a new type of foam, which reacts to the pressure applied to it, yet magically forms back to its original shape. Originally it was commercially marketed as medical equipment table pads and sports equipment, before having larger success as use in mattresses. This “slow spring back foam” technology falls under architectural innovation. It is commonly known as memory foam.
Radical Innovation
Radical innovation is what we think of mostly when considering innovation. It gives birth to new industries (or swallows existing ones) and involves creating revolutionary technology. The airplane, for example, was not the first mode of transportation, but it is revolutionary as it allowed commercialized air travel to develop and prosper.
The four different types of innovation mentioned here – Incremental, Disruptive, Architectural and Radical – help illustrate the various ways that companies can innovate. There are more ways to innovate than these four. The important thing is to find the type(s) that suit your company and turn those into success.
Data Discovery
Combining Data Applications with Data Engineering provides a solution composed of advanced data integration and preparation tools, semantic and graph data models, and data science techniques. The first phase, data mining, starts with accumulation of required source data from internal and third-party data providers or sources. Experts can efficiently explore and integrate data dictionaries and sample data as well as leverage keyword and use case centric search and filtering functionalities.
External data products add considerable value when brought into a single location. With such an ecosystem, data can be explored and accessed from traditional sources. Non-native data streams simultaneously to measure the impact on the accuracy of predictions, alerts, and ad-hoc queries. Essentially, this is a sandbox environment to discover and manage a multitude of external data sources to enrich customer data analytics models.
Data Engineering
Next, the data is engineered into blended analytics-ready datasets, a process we call data engineering. In this framework, a data engineering platform drives the discovery and integration process which enables business users to find, connect, and blend enterprise data into analytic-ready datasets. Data Engineering accelerates delivery of bespoke datasets from months to days and allows organizations to integrate all their data, including structured data from RDBMS, or flat files and unstructured data automates the data supply chain, allowing organizations to execute sophisticated data integration pipeline and publish analytic-ready datasets to downstream algorithms, applications, data scientists or other data consumers in the business in a lights-out automated way. Automation of the process enables scale by making it simpler and faster for more analytic-ready datasets to be delivered to the business more often. Data Engineering makes analytics-ready datasets available ad exported to other file formats, including CSV, JSON, and XML or in graphic formats as appropriate.
Data Science
Data Science that renders datasets to generate repeatable analytic insights should be brought together through process, business best practices, agile methods, and innovation—all of which are needed for unparalleled analytics-driven results readily available and inherently scalable.
Data Consumption “Use your information”
In the final phase, data consumption, we provide you easy access to the information and insights to assist in making strategic, data-driven business decisions. Today’s world is mobile, and Analytics Microapps deliver solutions that work across devices to provide you with secure access to the insights they need anywhere and anytime. With the use of microapps created for you, users can access and utilize outcomes from across platforms and current databases. This process provides businesses the opportunity to increase revenue, reduce costs, and prevent revenue loss while remaining compliant with government and industry standards.
Lets Increase Your Success
A combined Analytics Applications and Data Engineering solution solve organizational and business problems quickly with real-time data. These and other emergent digital methods and technologies have accelerated the pace of decision making and are moving from pockets of brilliance toward mass industrialization through approaches that operationalize data analytics one business decision at a time.
Predictive Modeling
The core ides behind the formulation of Predictive Modeling is, data that is being generated on a daily basis or the historical data that may contain the most relevant information for the present business scenarios in order to get maximum profit with suitable models and accurate predictions. The predictive modeling process involves the fundamental task to drag out needful information from structured or unstructured data.
With all this data, different tools are necessary components to extract inference and patterns, such as machine learning techniques are needed to identify trends in data and design model that estimates future conclusions.