Organisational data is the most important man-made resource that your business must essentially use in making lucrative managerial, operational and administrative decisions. With time, organisations’ big data grows exponentially, especially in industrial sectors where the enormous share of data is accumulated via IIoT devices like robots, sensors and other smart equipment. But most organisations are relying on the availability and talent of data experts, which is now gaining a healthy amount of scepticism because the power of AI and ML are challenging the complete commitment of data-dependent businesses to the professional guidance by data experts.
In the past, engineering, recalibrating, clustering, and deploying data models were tasks that were strictly related to data experts- who were required to be promptly accessible at all times. Being locked into the service of data experts requires a lot of investment because recruiting data science experts’ talent is expensive. Fortunately, after the advent of self-reliant data analytics, the use of digital data technologies to tackle data became less complicated, faster and easily trainable compared to relying on experts to do the job for you.
Today Cerexio aims at helping you understand the disadvantages of relying on data experts and how these disadvantages can be mitigated by using self-reliant data analytics tools to capitalise on data efficiently and optimally.
Transforming your organisation from being expert-dependent to self-sufficient in collecting, standardising, analysing and visualising data can be easier if you follow these three main practices.
Another main practice is being updated. Data practitioners of your organisation must always be updated in modern data preparation tools that can ameliorate the tedious, delayed and complicated data tackling experiences of the non-technical data users of your organisation. Better data preparation processes can benefit the company in faster digital transformations, automation and ease the utilisation of advanced embedded data analytical tools that are powered by AI and ML technologies. The constant evaluation of such trends can help your organisation in making the best out of data and saving copious amounts of time, money and effort in tackling data and making success-guaranteed data-dependent decisions.
Thirdly but most importantly, your organisation must make sure your data players are given the right training and consultation before they use self-service data analytical tools to manipulate organisational data. To use such data tools, data users (especially nontechnical users) must have their own set of skills and familiarity with using data tools. They must be guided optimally to engage in their own critical thinking, collaborate with their peers and disseminate information and govern data streams based on critical events and data-heavy priorities.
The basic and conventional expert-dependent data management is a long and time-consuming cycle. When an executive or a marketing player in your organisation needs data, he or she has to submit a request to the data practitioner, which is later examined, located and a data model is constructed, validated and visualised in a report. Based on the criticality and complication of the data request, a data retrieval that is run through a data expert can take hours, days or even weeks. However, data-dependent organisations no longer require a hectic process to extract data if they use self-service tools that are built to extract data by anyone despite their technical knowledge.
The most important benefit of using BI (Business Intelligence) Systems like ETL software powered by cloud platforms to access centralised data, perform event-driven queries and generate automated data-rich reports is breaking free from the reliance of data experts. Even non-technical users can easily and directly use self-service analysis tools rather than going on with the long process of requesting data, says a tech-savvy expert. It helps the data-relying players of your team to access a single version of the truth the way they want and the way they like. There are many benefits in allowing digital solutions that are not locked into the support of data experts, like:
Having a frictionless information system with on-demand streaming data flows is the main advantage of self-service data analytics. Recent digital computational technology like cloud-driven data analytics and edge analytics even allow users to manipulate and filter actionable data-based insights on the fly and confidently make decisions with real-time data. This ensures that your organisation puts a full stop to overdue data retrievals because these analytical capabilities do not require data experts to go through the long process of pinpointing data and directing them to you.
Another importance in using self-service data analytics is that these systems inherit a low barrier to data entry. It allows all systems of the organisations to be interconnected to data sources and other analytical tools. All the departments of your organisations, starting from HR, Finance and Accounting, Research and Development, Marketing, Logistics, and more, can all access data at a single point. The single point of truth enabled through the integration between data-rich systems like ERP, MES, EAM, WMS, Financial Systems, and other core systems allow all data players in the organisation to access data from different points and run queries based on their unique data requirements.
Self-service Data Analytics Tools and BI platforms allow your data experts to use their time, skills and effort on long-term, high-value projects that require complicated data handling. For example, they can focus on complicated data governance that ensures that the data is assigned to the right users and create optimal and efficient internal and external data structures that promote seamless data flows. This way, your company can ensure that the EDM (Enterprise Data Management) procedures, tools and p[olicies of your organisation enable high-quality precisions and timeliness of data flow with topnotch data security and prioritisation protocols.
Many data solution vendors are now offering a myriad of tools for data tackling, but only a few offer self-reliant tools that naturalise data management of organisations to a proficient task to a skill that can be earned by proper training and practice. However, the future of the business world is highly reliant on data, so their data tacklers must be highly-reliant on themselves too. This is why most data-centric organisations must rely on BI platforms and self-service analytical tools to automate data workloads and smartly manage data in order to embrace a culture that is unchained from expert reliance. This way, you can enter the new industrial age with more self-dependence, better self-growth and cost-efficient data management approaches that help you to be self-standing when narrating the success stories of your organisation based on data that you tackle independently.