Big Data has become a mainstay in many established organizations, putting a lot of pressure on small and medium-sized businesses to up their game. But considering their traditionally smaller margin of error and budgets, SMBs must get Big Data right from the start. What was once an enterprise-only activity now provides SMBs with potential opportunities to use Big Data analytics to gain a competitive edge in their respective industries.
CIOs are wondering whether they have the security, storage, and overall capability to carefully manage and analyze huge quantities of data. The following are some best practices that can help small and mid-sized companies find success using Big Data analytics:
Focus on the problem
Dealing with large quantities of data using analytics tools is both fun and insightful. But SMBs do not have the time or resources to waste on something that doesn’t solve real-world issues.
That’s why it’s important to ask the right questions. When you are sure about the end goal from your Big Data initiative, you can be clearer about the type of data you wish to collect and analyze. You need to ask questions that focus on your problems to achieve the desired business outcomes.
Gathering the requisite data and then translating the questions into variables for which data must be gathered is something that every small and medium-sized business needs to learn how to do.
Know the latest tools
Always stay updated on the newest high-end tools available to SMBs, especially the ones that can be availed from cloud-based solutions at a cheaper price. Advanced Big Data tools and infrastructure make it easier for companies to apply machine learning techniques for exploring large datasets that provide big business insights to even the smallest firms.
Look past the noise
Given the copious amounts of data, you need to differentiate between useful data and “noise” with high-impact business analytics. You must determine whether the data just correlates or if true causation exists. The latter will prove more advantageous.
Greater Big Data access
When you provide all employees with data access, it ensures you get the most out of your insights. Big Data is helpful across every aspect of business, but you need to know the limitations as well. The company might notice that cloud elasticity offers an environment where Big Data scientists crunch big volumes of data. However, there are instances where the cloud just can’t match up to Big Data processing. CIOs interested in analyzing and storing Big Data in the cloud may encounter architectural hurdles with respect to agility, operation, and capacity.
Get insights from business experts
To know which projects are both practical and promising, companies must work with business professionals to understand their opportunities and challenges. It’s also necessary to figure out the types of problems that can be resolved by different kinds of Big Data and analytic techniques.
Real-time data analytics
Innovations in the field of analytics and Big Data processing are gradually transforming the way businesses get value of their data.
There has been a considerable shift from descriptive dashboards and reports to systems that constantly analyze data to produce real time and actionable predictions. The key here is to remember that it’s not always about the data, it’s about the analytics as well.
The focus of Big Data analytics is to provide business experts with new insights they can rapidly transform into decision strategies that finally improve customer impact.
For example, visual tools responsible for creating decision trees enable business experts to rapidly segment customer populations through any mix of data-driven insights and policies.
Continuously assess and communicate
Effective collaboration only occurs when there is ongoing communication between IT and stakeholders. Goals may change midway through a project but it needs to be communicated to IT. You may have to stop collecting one form or data and gather another. You do not want that to continue any longer than necessary. Chalk out a clear map that breaks down the desired or expected outcomes at specific points. If the duration given is 12 months, make sure you check in every three months. This will provide you with a chance to review and alter the course if required.
Find a balance between human insight and Big Data analytics
The correct balance of human expertise with Big Data techniques elevates business performance and improves the organizational capacity to learn at great speed from data-based experiments.
Manage Big Data experts while maintaining compliance
Since Big Data is an emerging field, it does not lend itself to being self-taught unlike other kinds of programming. What this means is that there will be a shortage of qualified employees who possess the necessary Big Data skills. Make it clear who has access to Big Data and the level of access that different individuals must have. Data privacy is a key issue nowadays and all data privacy issues must be cleared before accessing sensitive data. Other governance issues should also be looked at, like turnover.
Implement Big Data analytics strategy
Big Data is rampant right now, thanks to the growing social media presence, cloud computing movements, and mobility. While the analysis of Big Data indicates increased revenue and big savings, there are several hurdles that first need to be cleared. You need to take stock of the major Big Data concerns and learn the best way that data can provide SMBs with an edge.
Big Data analytics: Still evolving
The creation of Big Data analytics has slowly evolved over time, so that it no longer needs small and medium-sized businesses to make a huge investment when it comes to creating expensive infrastructure and developing specialized skills. By leveraging cloud services, companies are able to let others securely take care of the underlying services and systems. This is a great setup that lets them pay only for the services and capacity that they need at the time. There is no need for them to reinvent the wheel; all that SMBs need to do is implement the Big Data best practices and take advantage of the momentum.