The concept of using data to make better management decisions is not entirely new. Before big data became a buzzword, business leaders had recognized the value of business intelligence and statistics. The term ‘big data’ currently refers to large data sets characterized by high volume, velocity and variety, as modern apps and big data technologies allow organizations to mine immense amounts of structured and unstructured data.
Yet, despite the hype around becoming data-driven and having virtually limitless access to all kinds of data, many organizations fail to use it to their benefit. Obviously, the barriers to big data adoption cannot be removed overnight, and overcoming them should involve a well-planned strategy.
In this article, we will explore the benefits of using big data across a number of industries, the challenges organizations face on their way to becoming data-driven, and the solutions they can use to adopt and fully embrace big data. Read on to learn more!
Most leaders of an organization believe navigating the sea of data is the keynote to staying competitive. Enhanced security, smart resource allocation, improved customer experience, predictive analytics, and facilitated IoT interactions are just some of the benefits of using big data analytics to improve business operations.
So why is big data important? Below are examples of how big data is empowering key business industries.
The use of big data in manufacturing enables companies to conduct predictive maintenance of enterprise machinery before it breaks down and thus avoid disruptions and downtime. Leveraging big data analytics also helps enterprises predict supply needs, monitor product storage conditions, achieve better product quality, and increase transparency of the supply chain.
In the energy sector, big data analytics effectively helps predict fluctuating energy needs and manage energy consumption. By gaining insights from data, enterprises can detect energy leakages and cut back on costs by taking timely preventative measures. Predictive maintenance using big data analytics accounts for timely repairs of costly equipment and machinery, as well as for enhanced security and non-disruptive performance.
Read also: How IoT is Transforming the Energy Industry
In agriculture, the use of big data analytics is tightly interconnected with the advent of IoT and drone technology. The amount of data captured by agriculture drones and sensors is truly immense; the analysis of metrics on plants, soil conditions, and weather conditions helps farmers boost the effectiveness of crop management, while predictive analytics help them plan for future harvests. In livestock farming, big data analytics help improve herd health and achieve better overall efficiency.
The use of big data in banking accounts for the emergence of an entirely new industry – fintech. By running data analytic algorithms, banks can gain insights into their customers’ credit history, determine how reliable they are, and make faster and more accurate investment and credit management decisions.
The importance of big data for fraud management in banking is difficult to overlook: advanced data analytics help bankers indicate non-typical customer activity and block suspicious transactions. Big data can also help banks and fintech companies achieve regulatory compliance, facilitate interaction with customers, and introduce personalized services.
Today’s healthcare is slowly transitioning towards personalization, as big data analytics enable physicians to prescribe individual therapy plans and medications based on the patient’s health metrics. The use of big data in healthcare also enables medical personnel to take preventive measures before patients experience serious health problems. Real-time big data analytics can read data from wearables and alert medical personnel when their patients’ health metrics display physical conditions which require attention.
The examples above demonstrate the transformative impact of big data on business. Yet, despite the obvious big data benefits, becoming a data-driven organization is no cakewalk. As enterprises embrace digital transformation, they also become aware of the challenges and roadblocks that stand in the way of big data adoption.
Legacy infrastructure and equipment
To function properly and deliver actionable insights, enterprise-grade big data apps need a flexible and scalable infrastructure to run on. Big data solutions require sufficient storage, compute and network capacities to capture, store, process, and analyze large sets of high-volume and high-velocity data. This task may require a full or partial infrastructure revamp, which entails investments.
Instilling a data-driven culture within the organization
One of the challenges organizations face as they strive to become data-driven is the lack of buy-in from employees and middle management. Not only does working with data require investment into software and upgrades to the existing infrastructure, it also calls for a culture capable of reading, listening, and trusting data in order to make data-driven decisions.
Too often, though, employees find it difficult to recognize why a certain metric or set of metrics is important and fail to trust insights derived from data.
Data management challenges
Deciding which metrics they need to monitor and collect is another challenge faced by organizations. Businesses also have to ensure that it is legitimate to use the data they collect and that its use complies with privacy regulations, such as GDPR in the European Union.
They also find it difficult to aggregate existing data into a consistent data pool, since it often resides in disparate databases. Retrieving this data is often difficult since databased and CRMs were built without taking this task into consideration.
Once the data is retrieved, in oder to manage your data efficiently, it is important to be able to transform raw data into actionable reports and dashboards. You may consider developing such tool in-house or using a 3rd-party service such as Zoho Analytics.
Zoho Analytics is a self-service BI and data analytics software from Zoho, that allows organizations get and blend data from multiple sources, and create reports and insightful, visual dashboards out of it.
Becoming data-driven often requires establishing new job roles and redefining existing ones. Unfortunately, coaching in-house talent is time-consuming and expensive, and experienced data scientists and engineers are rare. The ones available on the job market tend to command large salaries – in the US alone, the annual salary of an average data analyst exceeds $117, 000.
Digital transformation is associated with numerous challenges, and solutions seem hard to figure out. Yet, there are some acknowledged best practices for overcoming the challenges of big data adoption across organizations.
1. Retrieve existing data and identify data gaps
This task requires aggregating your existing data for further evaluation and extracting it from existing databases. As time consuming as it may be, it requires your specific attention. As of today, solutions like Data Warehouse Systems (DWS) are helping business meet this challenge by collecting pools of historic data into larger data lakes. Your next step will be defining which other metrics you will need to collect to fully benefit from data in your organization.
2. Decide on an optimal big data toolset
Upon defining the additional metrics, you will have to decide on your data toolset, which will also depend on your business type and the size of your company. For example, you may want to use Google Analytics to finetune your small business Internet marketing campaigns, while large enterprises will need complex, resource intensive solutions like Splunk or Cloudera for meeting enterprise big data challenges.
As of today, service providers have started offering tools targeted at specific use cases. The trend is also towards the ‘democratization’ of data analytics by introducing comprehensive visualization tools for data presentation.
3. Reallocate IT infrastructure resources
A lot of organizations run their big data apps on a public cloud infrastructure. Using cloud capacities, though, often requires a rethinking of an entire infrastructure model and replacing legacy applications and hardware.
Subsequently, a lot of organizations have adopted a hybrid approach to overcome big data security challenges – they use their on-premise infrastructure for storing sensitive data and running business-critical processes, while trusting other operations to the cloud.
4. Start with one step at a time
Your organization staff should strive towards adopting a data-driven mindset. People usually need time to realize why a certain metric is important, and to see the impact of big data on business and revenue. Yet, the adoption is much easier if the executive team is fully dedicated to tackling big data challenges.
Extending the impact of data across every facet of the organization will take time and effort. Big data adoption will resemble an endless cycle of deployments, tests and experiments. For some organizations, out-of-the-box solutions won’t work, and they will have to develop and implement custom-built tools for advanced data management. In this regard, partnering with a reliable provider of big data solutions could prove to be the best choice.
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