Modern day businesses are virtually snowed in by the amount of data they receive. It’s a staggering amount of information, topping 2.5 quintillion bytes every day. This is where data analysis comes in, allowing businesses to face the various challenges that arise from such rapid data knowledge growth.
The Internet boom of the ’90s woke the world up to a wealth of opportunity, increasing reach and influence far beyond brands’ usual abilities. Even though the Internet itself is 30 years old, there are a lot of businesses out there who still don’t understand the sheer power that comes from enlisting the help of seasoned experts to harness this data.
The Real Significance of Big Data
Disruption is the new “name of the game” in business. Noticing what could be done differently or be improved and figuring out how to do this better than your competitors is the way of the business world in the age of big data. When done by experienced professionals, data analysis brings a huge amount of value to businesses in their everyday practice which includes the following.
Better Management and Improved Capabilities
Companies who embrace big data to build their businesses benefit from working with experienced strategic advisors, to accelerate growth. These experts perform a range of services designed to analyze every aspect of a business and highlight areas for improvement.
Effecting Positive Change
Professional data scientists can sift through a business’ data knowledge to identify opportunities to create positive and productive changes, building the strength and effectiveness of companies in any industry. Individual opportunities for employees are identified, methods of improving productivity and morale are identified, and communications channels are smoothed to ensure that the basics are taken care of, before delving deeper into the requirements for further growth.
Making Better Decisions
Data analysis leads to superior decision-making that is informed by factual data, instead of the more intangible factors that influence the human mind. High-stake risks can be reduced, while previously unidentified opportunities can be pursued.
The analysis never ends! It’s an ongoing process that runs throughout the life of modern businesses and every project undertaken. Analyzing changes that have been made helps to eradicate the majority of the “teething issues” that can arise when large-scale alterations are made to a business’ DNA.
Making Decisions Using Big Data Insights
Partnering with experienced analysts makes all the difference, ensuring that constructive processes are developed, and generating a deeper knowledge of the various factors that create success.
Machine and deep-learning are taking over the business world, with a wide range of applications that cover almost every sector. Machine learning has evolved to the point where it can manage customer service, logistics, medical records, and create original news reports.
Gathering real value from analytics requires strong talent, immense fortitude, and no small amount of time. Many businesses are struggling with at least one of these requirements. New technologies boost productivity but can result in job losses or reassignments.
Data Processing Models
Data modeling involves creating conceptual representations of data objects. These are easier to understand and store in databases. These models not only make processing data far simpler but also go a long way to maintain the basic operating rules and guidelines within your business.
Consistent semantics, best practices, naming requirements, and overall security are important in any business, and modeling data in this way avoids unnecessary time wastage. There are three types of data collection models, each with its own benefits:
Conceptual Data Processing Models
This a great model to define what your systems are made up of and is usually used by key stakeholders within companies to organize, oversee, and define best practices.
Logical Data Processing Models
Logical models help data architects and analysts to understand how systems can be implemented and involves developing a map of rules and database structures for the business to follow. This can be used for any database management system (DBMS).
Physical Data Processing Models
This is the actual process of implementation, and how it will be put into place. Database analysts and developers use this to put a working process in place for a specific database management system (DBMS).
Best Practices of Data Collection, Structured Storage, and Processing
Businesses who truly embrace data analysis are reaping the benefits of their efforts, allowing new methods to improve their core competencies, launch new business models, predict trends, and stay ahead of their competitors. These are the businesses who forge strongly ahead, actively seeking out new ways to enter new industries, bridge the gaps between them and their clients, and add new lines of business.
These are key expansions that are increasingly blurring the lines of traditional business sectors. Here’s how you can stay ahead of the curve and make sure you’re always making intelligent decisions based on your real core data.
Set up a Solid Workflow
You have to have a process in place that enhances productivity and makes your data collection simpler. Expert data knowledge analysts know the value of spending some time on this procedure, to ensure its easy to update, navigate, and extract data from. This types of data collection will save you huge amounts of time in the future.
Be Consistent and Practice
When a workflow has been set up, it’s important to commit to it. There may be some “teething issues” initially, but this can be largely mitigated by remaining dedicated to the process and riding these out. Improvements can be made constantly as you learn – this will ensure that you develop a system that truly works for your business.
Keep the Best, Leave the Rest
You only need the data that has an impact on your business and future goals. The rest is background noise and can slow you down. Only capture the data you really need, which will allow you to focus on the prominent features of your company without wasting time on irrelevant details.
Security Is Important
Storing data electronically is fantastic for reducing paper waste, clutter, and wasted time, but it can be risky when security isn’t given a high enough priority. Data knowledge experts make sure you are protected against data breaches at all times by ensuring your security protocols are up to date.
Ask the Professionals
Creating processes can be complex, frustrating, and even fruitless when attempted by business people with little to no experience in this area. It’s far more productive to seek out trusted, experienced data analysts. This saves you time that would otherwise be wasted through trial and error and learning the ropes.
Businesses are scrambling to increase their ability to cope with the influx, expected to increase much more rapidly thanks to the rise of the IoT (Internet of Things). As it stands, the last two years have generated such a huge amount of data, that it accounts for 90% of the data in the world.
Every day, The Weather Channel receives 18,055,556 forecast requests, Spotify adds another 13 new songs to their database, 600 new Wikipedia page edits take place, and Uber passengers take over 45,788 journeys. Every search engine query, every online interaction, and every “like” adds to the amount of data collected – and it all needs to be processed.
Potential disasters can be averted, and upcoming trends are fully recognizable when the ongoing analysis is made a priority.
Data Is Important Only When You Can Extract Information Out of It
The internet brought the world to business’ doorsteps, often overwhelming staff and highlighting the importance of morale in maintaining a productive and profitable edge. Businesses who were born into this data-rich environment have an easier time of it, as the traditional businesses have to do a lot more to change their systems and standards to fit in with this new environment.
Adapting to data-driven processes is never easy, resulting in companies having spent a lot of money on new systems, and never truly taking full advantage of their new abilities. A powerful strategic vision is required to tap this seemingly endless resource and use the resulting data to its fullest potential.