As software development in Sri Lanka continues to focus on building solutions that are scalable for the future, cloud support services further fuel the trend. Agile software development is now possible with hosted services now part and parcel of the IT industry, as data can be centralised and versions can be controlled on a real-time basis. These capabilities prove to be beneficial for every IT endeavor – and data analysis is no exception.
Business intelligence, therefore, plays a pivotal role in a company’s ability to be astute – especially when making decisions that are sensitive to time, amidst fierce competition and economic instability. Add to this the proliferation of digital channels (such as social networks) that saturate options for consumers to consider. The slightest lag can cause massive abandonment rates, so building a strategy where your business’s products and services stand out from all the noise is imperative to its success.
While reporting and analytics tools can be built to make sense of otherwise raw numbers, maintaining these to deliver reliable results depends on data hygiene and integrity. Due to this, business intelligence isn’t a one-time project that can be set to the side after it has been deployed; it needs to be constantly monitored to ensure data governance is adhered to, and the quality of results isn’t deviating with time.
Self-service business intelligence, on the other hand, takes conventional business intelligence a few notches higher; by giving employees the autonomy to build their own reports, your organisation is well set to gain access to results in a timely manner. However, venturing down the path of implementing self-service BI requires many variables, all of which need to be addressed before a reliable system is ready for use.
Self-service business intelligence allows staff that aren’t tech-savvy, to access data, create queries, and generate reports – without obtaining any help from the IT department. Staff can create queries by accessing an interface that is simple enough for non-tech-savvy individuals to use and generate a variety of visualisations – including dashboards that are updated on a real-time basis.
With freeing up IT personnel from ad hoc report requests and easy access to data being the key objectives of self-service business intelligence, organisations need to ensure that data is governed properly throughout. Having hosted solutions for centrally storing and managing data is also highly resourceful since databases will be accessed from multiple departments, and likely at the same time. Being a dedicated AWS partner is advantageous for such a purpose, as any manner of service can be obtained from a variety of virtual infrastructures for comprehensive data management.
Another characteristic feature of self-service BI ensures that the interface developed is accessible by users across varying skill levels and departments – from assembly line workers in a factory, to c-suite executives in the boardroom. This means that a variety of datasets can be accessed by different levels of staff, to reveal insights that are relevant to their line of work.
The key difference between traditional and self-service business intelligence lies in the system that is built to obtain results from data – along with the time frame involved from initial request to final report. In traditional BI systems, IT teams hold sole stewardship of both data and reporting. Staff need to submit a request based on a requirement gathering process that is established by IT, who then sources the correct datasets, creates the appropriate models and then generates a report for fulfilment. From request to report, this entire process can take days, if not weeks.
On the other hand, self-service BI consists of a user-friendly interface that employees of all technical skill levels can interact with, along with relevant datasets that are already connected for providing immediate results. Queries can be made by navigating through this easy-to-use interface, with reports being generated instantly for business decision-making. Since queries and reports are facilitated by staff themselves, the turnaround time for self-service BI is significantly shorter than that of its conventional counterpart, with SaaS solutions topping only a few seconds, creating record time.
While it is pretty clear that traditional BI is completely managed by specialist IT and BI teams, the interfaces, datasets, and underlying systems powering self-service BI are also built and managed by the very same. Therefore, IT and BI teams, including business analysts, all play a crucial role in maintaining BI systems for an organisation – be it traditional or self-service.
While no two BI systems from different organisations are identical, there are still some components that are essential for building the foundation that is required to facilitate self-service BI that produces reliable results, is adaptable to different forms of data visualisation, and is scalable for the future.
For any kind of BI system to work, relevant data repositories and inter-departmental applications need to be integrated. Depending on what your business specialises in, there may already be existing software applications in place to manage tasks, automate workflows, and monitor operations. For more generic use cases, inter-departmental applications such as CRMs, accounting/invoicing software, HR software, and even fully-fledged ERPs can be integrated to create pipelines for data to flow. Database management systems are also another key application that need to be integrated, so your self-service BI system has the data it needs to reveal insights.
Now that you have the datasets you need, it is time to build the connections that are essential for data crunching, between these datasets. Based on the nature of the queries that will have to be processed, data models need to be built by IT and BI teams. In the case of data analysis which is empowered with AI and machine learning technologies, algorithms need to be ‘trained’ with big data, before deployment. Several trials with varying datasets will likely also be part of the process to render more intelligent BI systems, as algorithms will have to be ‘taught’ to reveal results that are free from bias and within context.
Albeit requiring more resources in terms of expertise, time and money, AI-based data analysis can be highly beneficial for businesses in a number of ways. For one, bots trained with machine learning can be autonomous as they self-learn with new data that becomes available over time. Intelligent data analysis can also pave the way for predictive reporting, by forecasting trends that can then be considered by business leaders for wiser decision making.
With back-end databases integrated and modelled, a user interface is now required. Ideally, the design phase of this interface can be executed in sync with all the data preparation processes mentioned above, especially if your software development lifecycle is agile. The interface of your self-service BI platform has to consider the needs of different users who are expected to interact with it, so that the user journey is smooth, predictable and involves the least number of steps possible, once a query is made.
During this stage, it is also important to consider the security parameters which shall authorise access to relevant personnel, as well as the interface that will be needed to accommodate the very same. Since any BI platform is a touchpoint for accessing confidential business data, it is imperative that all entry points adhere to robust security protocols when granting access to relevant staff, while having proactive mechanisms to deter any malicious activity.
As datasets increase in size (especially through big data), it is important to make sure that data now sourced is free from errors, and usable by the algorithms which power your self-service BI platform. Therefore, maintaining your data repositories at scale is a task that your IT teams need to commit to on an ongoing basis, and as part of a healthy DevOps cycle. Also, adhering to relevant regulations is essential for your business to be compliant and functional on a state, federal, and even an international level – thereby requiring stringent coordination between your IT and risk management teams, also on an ongoing basis.
Although self-service BI aims to free IT personnel from report requests, it doesn’t completely eliminate all responsibilities associated with generating reports that are required for your business. As ad hoc report requests are now self-managed by staff, IT teams, together with business analysts and dedicated BI specialists, now bear responsibilities surrounding the privacy and governance pertaining to sourcing, accessing and storing vast amounts of data. Depending on where your business is located, data regulations will also apply – and this increases implications if you function internationally.
With IT teams already responsible for ensuring strong security perimeters within the organisation, further enforcing access controls to data over a self-service BI platform is a task that now needs to also be undertaken. Irrespective of whether your organisation uses traditional or self-service BI, monitoring the pipelines which gather data is something which IT and BI specialists will always have to do.
With the passage of time, standards set to determine how, when and from where data is gathered could deviate – and it is up to IT/BI teams to ensure these standards are constantly adhered to, with re-alignments done if required. The possibility of data hygiene reducing with time is highly likely in the case of big data, as sources which generate vast amounts of data with the simplest/smallest interactions (such as mobile apps and social networks) can result in erroneous and/or disparate repositories. These problems can render data unusable, which can subsequently impact the quality of any BI system that your organisation has in place.
Complying with data regulations is another crucial task, as regulatory bodies on state, federal and international levels alike expect instructions to be precisely followed – what with users’ data being collected in vast amounts across the digital landscape. IT teams need to constantly monitor this, as any oversights can have both legal and financial impacts on your business. While self-service BI will be adequate for daily/routine report queries, the expertise of your IT team will be needed for queries of a more complex nature. This is especially true when AI and machine learning is being introduced to all your data; bots will need to be trained with technical expertise from your development and IT teams, so that the results obtained are accurate and therefore reliable.
All in all, the role of IT and BI specialists are still vital in the presence of self-service BI. From maintaining data hygiene to ensuring the adherence to data regulations, the IT and BI expertise in your organisation will lead the efforts needed to preserve data integrity for a contribution toward the smooth and reliable functioning of your BI systems.
As companies seek to shift gears for enhanced growth in an ever-changing marketplace, business leaders require quantifiable results to influence their decisions. Time is key, and waiting for numbers to be crunched can lead to downtime and therefore, missed opportunities. With custom applications requiring hosted infrastructure and real-time database management systems, software outsourcing in Sri Lanka has long since been at the forefront, as clients’ business needs are scaled through dedicated partnerships with a leading cloud service provider.
Self-service BI is one such example where customization meets immediate results, so that businesses need not wait for the answers that will help drive wiser decision making. Consisting of a user-friendly platform which staff can access to generate reports, self-service BI goes a notch above traditional BI by offering accurate reports much faster – and without the need for technical expertise from an IT specialist. As IT teams are freed from the daily grind of attending to report requests, staff members across the hierarchy now have instant access to valuable insights that they can use to improve business outcomes – without sacrificing time.