The importance of analytics in the modern world cannot be overstated. A good model can draw out relevant patterns and descriptions from seemingly overwhelming mountains of data – drawing light from darkness, if you will. However, not all analytics are built equal. Like any process, it takes in an input and processes it before returning an output. To ensure that your process is lean, streamlined, and gives the best value for all stockholders, a modern data stack (MDS) is needed.
Regardless of the size, structure, or industry of your organization, if you’re using analytics in some capacity, you’ll need an MDS to boost it. To help you understand how to arrive at the best solutions available, here are 4 ways modern data stack improves your analytics:
Reducing operational costs
Aside from information reach, analytics systems are usually assessed by the cost they save for their organization or the profit they can turn in. In improving data analytics for all concerned shareholders, reducing costs is one of the most visible changes brought about by a modern data stack. With a state-of-the-art MDS, you can cut down on your data engineering costs by almost 90%, as is the case with the Ignition Group, one of Africa’s largest providers of media and telecommunication services.
With a streamlined stack, companies no longer need to design, build, implement, and maintain traditional data pipelines. An MDS also eliminates the need to normalize data from existing denormalized APIs, which equates to reduced costs for the involved databases.
Aside from the specific savings regarding pipelines and normalizing data from denormalized sources, a modern data stack generally comes with greater savings compared to continuing or implementing new on-premise setups of an equivalent capacity. Usually, MDS is built on flexible sets of technologies that are in turn, cloud-based. This means that most of your data are handled on secure, online servers instead of an on-premise setup. As a business owner or a person in charge of maintenance and networking, imagine the costs involved with setting up and maintaining your own server – from the design and configuration, to hosting, and even the costs of maintaining the server room. These are all costs you can potentially do away with a modern data stack solution.
Increased organizational data literacy
One of the persisting challenges in the field of data science is the translation of relevant data into formats understandable to all relevant parties. In an organization, it’s common to have people well-versed in building computer networks, there are experts in creating systems that process large amounts of data, and there are people whose business acuity keeps the entire company afloat. The challenge is to bring these different professionals on the same page, especially in situations where a decision must be made for steering the organization forward.
This advantage is magnified in the fields of finance, healthcare, and security, rapidly-growing global industries which are heavily dependent on business intelligence (BI) and other forms of analytics. Using a modern data stack makes it easier to design BI and analytics tools to be more intuitive and easy to use. These designs and controls make the tools accessible for end-users of different backgrounds. In previous situations where salespeople had to consult their analysts, now they can have access to the data directly in a form that makes sense to them. This cuts down on the lead time and increases the competency of the regular users.
Also, a healthy collaboration between different departments is becoming one of the top business intelligence trends. To drive your analytics forward, you will need a fast and accurate response from the relevant teams. If they can access data in real-time and act upon it, you can rest assured that your company won’t miss any opportunities moving forward. This is not only made possible, but easier, with the use of a modern data stack.
Reduced downtime, increased analytics projects turnover
In any business, downtime is equated to loss in productivity and in turn, loss in profit. The use of a modern data stack in your analytics greatly improves data reliability and lifts the burden of ETL maintenance from your organization. With an MDS implemented, automated data pipelines immediately capture and respond to changes in your schema and API even without human intervention. This makes sure that any issues are immediately addressed and notifications sent to relevant recipients without having to wait for a manual ping from an end-user. This makes sure that companies no longer need to fear pipeline downtimes or data gaps which adversely affect analytics.
Of course, with less downtime and fewer resources required to maintain and monitor analytics and data streams, teams can now focus on developing new analytics projects. This is important in fast-paced fields such as cybersecurity, the improved downtime means that companies can access more data that will enable them to develop and implement cross-analytics to examine criteria and patterns previously unavailable to them. To put it simply, it could empower companies to develop tools that could give them an edge in the industry and in turn, give a better service to their clients and a greater return for their stockholders.