3 Steps to Faster Insights in Data Analytics

3 Steps to Faster Insights in Data Analytics

In recent years, we have seen wide adoption of data analytics. However, most organizations continue to find it challenging to quickly yield actionable insights. Some issues that have been most often cited for this include:

  1. Poor data quality: While preparing data for analysis, users often discover that some data elements are missing or erroneously captured. There is also often a lack of standardized definitions for data elements, which leads to the misinterpretation of results. Users end up spending more time correcting data integrity issues than deriving insights from the data.
  2. Data stored in separate silos: Due to organic growth over time and the lack of a central data strategy upfront, data often resides in various operational and storage systems. Having data spread across multiple disjointed systems causes the blending and merging of data to be difficult and inefficient, inhibiting the quick discovery of correlations and hidden patterns.
  3. Underestimating the effort required to deploy machine learning models: Except for a handful of advanced tech companies, few organizations have the expertise and scale to be able to automate the deployment of machine learning models into a production environment, monitor model performance over time, and perform continuous re-training and model re-calibration to ensure that models continue to be effective and relevant.
  4. Lack of a common set of analytics tools: Organizations that allow the use of different data analytics tools may end up hindering collaboration and the sharing of insights.
  5. Over-reliance on data scientists: Many organizations rely on a limited number of data scientists to execute their analytics initiatives, resulting in a bottleneck that inhibits the rapid adoption of analytics.

In this post, I’ll cover some concrete steps that stakeholders can take to improve agility in analytics, so they can reap the benefits of data and analytics for faster decision execution.

Enabling Analytics Agility through the Right Set of Analytics Technologies

Data Virtualization

Agility in analytics can be achieved by reducing the friction of data access. To facilitate and simplify access to disparate data sources, consider exploring technologies such as data virtualization to add flexibility to an organization’s existing data architecture. Data virtualization enables users to access, query, and integrate data from various sources, regardless of whether the data source is on-premises, on the cloud, or across various geographies. This approach creates a single, enterprise-wide platform to enable connections to any kind of data source, combines various data types, and enables data to be accessed centrally and to be consumed in various modes, including dashboards, reports, or advanced analytics use cases. A data virtualization layer can also provide data catalog capabilities. This helps to reduce the time typically spent on hunting for data from various data silos, and it drive self-service, which in turn results in higher productivity. With data virtualization, new data sources can be added significantly more quickly than using the traditional extract-transform-load (ETL) method. Data virtualization can also simplify the data-model-management process. According to Alex Hoehl, senior director at Denodo Technologies, data virtualization solutions such as the Denodo Platform “simplify the data management process by providing a single access layer which enables data source access, data security, and data governance to be managed from one single place.” This means that data access administration activities such as adding new users, changing access rights, monitoring audit trails, etc., can now also be done more quickly and easily.

A Unified Data Analytics Platform

To harmonize the hodgepodge of analytics tools used across an organization, consider implementing a consistent, unified data analytics platform. An ideal data platform should support data preparation and data blending. It should also incorporate robotic process automation to automate certain manual processes, especially for mundane, time-consuming data-preparation tasks. At the same time, the platform must be sophisticated enough for data scientists to collaborate on advanced analytics modelling, enabling machine learning models to be shared easily across the organization. Most importantly, a unified data analytics platform should automate model deployment, monitor model performance over time, and automate model re-training and re-calibration for ongoing model maintenance.

Some platforms have the added bonus of providing a code-free or code-friendly environment that enables drag-and-drop functionality, making them as business-user friendly as possible. This is an important consideration in selecting the right analytics platform.

Enabling Analytics Agility by Empowering People

Two of my colleagues explored the topic of data democratization and data literacy in another post, on the NCS website. They stated that being data literate not only means being able to read and analyze data, but more importantly, it means being able to “argue” with data – to challenge what the data means and to use data to support a hypothesis. Data literacy is important not only for data scientists and CEOs but also for every staff member on the ground, as they understand the context of the data collected and can come up with unexpected insights on how it can be used.

Today, more and more tools are geared towards “citizen data scientists” – business analysts who may not be armed with PhDs but who are just as capable at delivering actionable insights from a combination of enterprise and external data. Citizen data scientists are a direct result of the data democratization movement, and they are aided by an array of AI-driven tools and technologies.

Above, I covered the merits of a unified data analytics platform. Having a platform that business end-users can easily use would further accelerate analytics agility because it reduces the reliance on specialized resources such as data engineers and data scientists. With some training and upskilling, data analysts and business analysts can tap into the self-service capabilities that such a platform provides. Now, instead of waiting for data engineers to prepare the data, and for data scientists to provide the insights, business users can be empowered to do these data-related tasks themselves, increasing productivity and agility.

Andy MacIsaac, public sector solutions marketing director for Alteryx, a market-leading business-user-focused analytics platform solution, summarized it well: An effective unified analytics platform “provides data analytics, data science, and process automation capabilities across the entire digital transformation capability continuum and brings together business users, citizen analysts, and information consumers to accelerate organization-wide outcomes.”

Enabling Analytics Agility through a Robust Data Governance Process

Data scientists reported that on average, 26% of their time is spent on data cleansing. This represents the amount of time that can be saved if there is data quality in the first place.

Broadly defined, data governance is the exercise of authority and control over the management of data assets. The proper management of data, in accordance with well-established policies and best practices, is key to empowering an organization with information and analytics capabilities. Effective data governance helps to avoid inconsistencies and errors in an organization’s data, which could jeopardize the accuracy and completeness of the data insights required for sound decision-making. Data governance covers internal policies and procedures that ensure data is secure, trustworthy, well-documented, effectively managed, and periodically audited.

Data is a valuable asset to any organization, and engendering trust in data is critical for driving widespread adoption of data analytics initiatives. By enabling data to be managed with proper governance processes from the outset, organizations can provide staff with access to trusted, high quality data, which provides users with the peace of mind to make accurate, data-driven decisions without having to first spend time validating the accuracy and completeness of the data.

Conclusion

Often, there is no single reason why an organization might not be able to achieve the promise of agility in its data analytics initiatives. The impediment is usually the result of a combination of factors, as listed above. However, with the right data and analytics technologies, process improvements, and a shift in mindset towards data democratization, organizations can take positive steps toward building more agility in data analytics.

Loo Soo Kiat
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