Data Analytics has become the game-changer for businesses across multiple sectors. It has empowered organizations to heightened levels of both internal and market awareness, ability to foresee trends and act on them, and improve business elements to generate more earnings. Despite all these benefits, there are still pitfalls for anyone who wants to work their Big Data, if they don’t have the right skills, mindset, and strategies to perform analytics.
In this article, we have listed down 5 ways to improve Data Analytics projects with best practices that are proven to work. These will allow businesses to maximize their optimization initiatives with Data Analytics, improve the whole process, and gain better insights that are actionable and translate to real-world value. Being data-drive is clearly one of the best strategic moves for companies to pursue, but knowing these simple yet effective habits can give you the edge to go beyond the competition.
As with any business endeavor, diving in head-first into whatever project without knowing what the purpose of the project is a recipe for disaster. This has become one of the most common problems facing the large number of businesses who are trying out Big Data Analytics for the first time. Not knowing what to look for, what to expect, and what end-result you want can lead a budding Data Analytics projects nowhere.
Clearly laying out your goals, with both short-term and long-term in mind, is the first step in addressing this dilemma. Knowing what you want to get out of big data enables your businesses to identify which data sets to analyze, which tools to use, and gives you a definitive perspective on where you are heading on your project. Flexibility should also come to play when setting objectives. Letting yourself have options to adjust certain aspects of the project depending on factors such as existing resources, apparent risks, and immediate results, can make sure that your Data Analytics returns significant value.
It is recommended that companies start with simple and focused project scopes to better gauge capabilities. This allows the set up of benchmarks for future, more comprehensive projects, and minimize future risks. Preparation combined with adaptability comes a long way into ensuring you get accurate and precise results from analyzing your data.
There are some benefits to having an on-premises data infrastructure, such as the feeling of immediate access and privacy to your data sets. These, however, are relatively superficial compared to the definitive advantages of integrating a cloud-based (or hybrid, if you already have the hardware in place) infrastructure for your Big Data. Not only that, having to maintain your own infrastructure can result in even more risks than doing the alternative.
The US has spent $124.6 Billion on cloud services in 2019, accounting for more than half of the world’s spending total on cloud services
Embracing cloud infrastructure for your data storage and data engineering needs frees you up from the burden of having either a bloated or an overworked data team. Smart companies even opt for outsourcing their data science processes to expert service providers who have the people with the right skill sets to perform multiple levels of Data Analytics. These initiatives free your people for wasteful and repetitive tasks and allows you to focus them on turning analytics results into workable models that actually improve your business.
Data Silos are data storage areas that are isolated at a specific sector within an organization. This data can represent any number of information that is kept and controlled by singular departments within your business. This presents problems when it comes to using Data Analytics because those lone data sets can signify critical variables affecting the performance of business processes you want to optimize.
91% of companies in a survey report that the ability to integrate data from any source is critical to their strategic goals
Identifying these Data Silos is the first step into building connections to variables that affect the success of your business. Consolidation does not mean you have to move these isolated data sets into a single data monolith for your whole company. Technologies and tools can provide effective data integration while maintaining existing storage infrastructures. This saves you the resources from committing to any drastic change while keeping your current systems useful.
Another common issue that plagues first-time adopters of Data Analytics is getting inaccurate results that if used, can turn into failed models. The primary cause for this problem is having “garbage” in your data sets. These are data that are either outdated, false positives, damaged, or incomplete. These dirty data can present hazards that will dramatically decrease the value returned by your analytics project.
Data Scientists spend 60% of their time on cleaning and organizing data
The importance of having clean data, data taken from actual respondents and sources that have been properly formatted and organized, cannot be overstated. There are a lot of tools out there that can help automate the Data Cleaning process but finding the right one and the right people that can use it could be a tedious experience. Once again, espousing strategic partnerships with B2B Data Analytics firms can help you skip the hard parts and deliver valid insights for you to work on.
Having everyone in your whole team or company, and not just specific departments, adopt a data-driven culture can be your the crucial factor in breeding success with your Data Analytics. Having inputs from all levels of management, makes your analytics project touch base with the people in the specific sectors you are trying to improve. This approach also has the added benefit of demystifying Data Analytics to those employees who are not familiar with the concept. This helps them ground their expectations together with fostering a deeper cooperation between teams working on implementing optimization initiatives taken from your analytics.
This list is but the tip of the iceberg among a multitude of other good habits to adopt when it comes to Data Analytics. FilAm Software Technology, with more than a decade of experience in Data Science, recommends that businesses emulate these practices to achieve greater success in performing Data Analytics. Companies should make sure that they are being stringent in conducting analytics projects while rapidly keeping up with emergent technologies that improve upon analytics processes.
Do you have any other ideas you think could help decision-makers in adopting Data Analytics? Comment below and let us know your thoughts.