In recent months, the importance of agility has been a much discussed topic in terms of business.
Although understandable given the great changes that are needed in no time due to the outbreak of the pandemic, one might think that it is only during the period of great change that companies should focus on this agility.
In fact, agility has always been a key feature for the success of the business. No company is immune to the risk of becoming redundant: being flexible becomes essential to managing market changes, technological innovation or even changes in work practices.
Many companies that have achieved success worldwide are characterized by the agility demonstrated: we think of Netflix, who initially sent physically DVDs (there are few to remember it) and who now defines the canons of streaming, to cope with the change of the Change that they have not faced competitors like Blockbuster, with a different destiny.
Whether it is a response to changes in the workplace or to predict long-term trends in customers, agility remains the basis for a company’s ability to evolve and adapt to protect and improve performance and productivity.
Instinctive business decisions
However, the adoption of an agile approach is not without risk. And these are more acutely felt in changing scenarios, such as the current one, James Fisher, Chief Product Officer of Qlik explains.
In fact, situations of this kind highlight the way decisions are made, since 71% of corporate leaders often rely on their instincts to make decisions, putting change of course at risk and taking their company on a completely wrong path.
To make decisions that improve productivity and performance with agility and security, it is essential to truly understand the circumstances in which we operate, and this can only be achieved through the access and analysis of accurate, clean, reliable and timely data.
Insufficient data, wrong decisions
A research conducted by Qlik in collaboration with IDC has highlighted the significant and pervasive problems that organizations, globally, face in creating a strong data pipeline that identifies and prepares raw data for analytics
Over half (57%) of the companies surveyed considered that they had found and captured the majority (70%+) of the value data sets produced within the organisation, but the difficulty in acquiring and processing the data is then reported widely.
Yet, when working in the right direction, the results are concrete: it has been shown that successful investments in data management and analytics improve both productivity and performance. Three quarters of the organisations reported that operating efficiency, revenue and profits improved on average by 17%.
According to Fisher, there are three key considerations for organizations that try to improve their ability to use analytics for a data-based approach to agility, which arise from as many questions as a company has to ask itself to define itself as data driver.
Do you trust the data?
Although the goal of becoming a truly data-driven company, often data are not questioned as you should and, consequently, you make decisions based on inaccurate insights.
Before using the data analysis to make truly informed decisions, it is important to ask whether the baseline data is part of one of the common traps that affect their reliability. Are they complete? Correct? You sure? In our survey, corporate leaders cite these as the main challenges they faced in the acquisition and processing of raw data (40%, 42% and 38%).
It is also important to ask what are the best data to be used to make decisions. Almost all global organisations (94%) are struggling to identify potentially valuable data sources. This is the area of the data pipeline where a quarter of companies plan to make the most investments over the next 12 months. Understanding what data is in your organization’s possession is the only way to make sure you are using the best information available to you to make decisions.
Are you moving fast enough?
It is not sufficient to have the data, it is also necessary to ensure that they are up to date and relevant at the time of the decision. Almost a third of corporate leaders report that not having data in time is one of the most common reasons why analytics projects fail.
In the past, access to ready-to-analyse data from some sources (such as transactional data from ERP or CRM systems) could take six to nine months due to the cumbersome extraction, transformation, loading process (ETL).
With the Change Data Capture (CDC), organizations can transmit information in real time, regardless of source or scheme, data warehouse or cloud-based platforms, where they can be prepared and provided automatically for analytics. This process reduces the time required to transform raw data from ready for analysis from months to minutes.
Has the team been trained to use data?
Ensuring that your team has the skills to decide in an informed way is an integral part of the transition to a decision making process that is agile and cross-cutting to the whole company. Indeed, the need to improve the training of employees was voted by business leaders as the second most critical area for the success of data analysis projects.
This is perhaps not surprising given that only 21% of the global workforce have full confidence in their data literacy skills and, if overwhelmed by data, employees report that they have found alternative ways of completing the activity without using data (36%).
Educating and putting your team in a position to understand and question data is crucial to identify opportunities to increase operational efficiency and productivity, as well as to identify new trends, which will allow your company to become truly agile.
Fisher probably concludes that there is not a single company in the world that did not have to make agile decisions in response to the pandemic crisis. Yet, of these companies, how many can say with certainty that they have been based on reliable insights?
It is important that companies learn from these last months of rapid agility, understanding where the losses are in their pipeline of data that prevent such decisions from being supported by data.