Data Economics

Data Economics

Sapir Friedman, Client Solutions Manager & PM Team Leader at Matrix DnA image
Sapir Friedman, Client Solutions Manager & PM Team Leader at Matrix DnA

It is said that data is the new oil of the 21st century. Here are a few tips to help you make the best use of this treasure in organization's own backyard.

For more than a decade, we have been told that data is the new oil. But, if there is one lesson we’ve learned after more than a year of Corona it is that data, while necessary, is not sufficient on its own, because raw data is not valuable in and of itself. True value is only created when data collection is carried out comprehensively and accurately, and when the data body is cross-referenced and linked to other relevant bodies of data. Data that gets the ‘right treatment’ enables an organization to be proactive and respond in a timely manner to market forces, becoming a very significant tool for decision-making, improving customer experience, streamlining and above all – driving profitability.

Despite the tremendous inherent potential of data, only a few organizations know how to handle it correctly. A study conducted by IBM involving some 13,000 executives in organizations around the world found that organizations that excel at properly connecting data to their business strategy, operations and corporate culture are more profitable and grow faster. And yet, only 9% of the organizations that participated in the study belonged to this group.

If you are in the remaining 91%, it is important to understand that, in order to derive more benefit from the data in your organization, you need to formulate a data strategy. We’ve put together some tips for you to get started.


1) It’s time to connect your data strategy to your business strategy – and yes, you do need a data strategy!

Whether you are in the initial stages of setting up, or you have reached a stage where you want to refine and upgrade your data systems, the first step is to examine your business strategy and define the goals you want to achieve (such as improving profitability, increasing sales, identifying barriers, etc.). Data systems that are not connected to the organization’s business strategy can become a burden instead of a first-rate business tool.

Once your business goals have been set, it’s important to define key performance metrics as clearly and accurately as possible, ideally before you move on to the task of data collection. There is a lot of data in organizations and most of it may not be relevant to the meeting of your defined goals. That’s why filtering is so important. We do this by learning how data analysis is performed in the organization, what difficulties arise in the process, and how conclusions are reached at the end of the process.

Once we have defined the business goals and performance metrics, and learned how data is currently analyzed in the organization and the difficulties that accompany the process, we have all the tools needed to define the organization’s data strategy, and to start moving to the execution phase and building the system.


2) Insist on a flexible, self-service data system that enables users to work independently

It is very important to make sure that the data system that is developed for you is dynamic. A static system, advanced and detailed as it may be, will not be able to answer every user question that arises, or remain relevant over time. It is therefore important that your system is flexible enough to accommodate future developments and leave room for users to work independently.

It is recommended that users explore the system independently. Make sure that innovative tools – for example the ability to create dynamic reports, eliminating the need to contact developers to produce each report individually – are demonstrated to the intended users. Also, try to encourage system users to present conclusions through the same tools. For example, there are tools that allow you to ‘tell a story’, by creating a set presentation that is automatically updated from the data, and if necessary, present data from the system easily and quickly. Proper training in how to independently use the system and development of tools will enable users to make optimal use of data, and get the most out of it.

2) Invest in implementing the data strategy within your corporate culture – when it comes to data and the automation system, employees can become allies or enemies

People do not like changes to their work routine, even when those changes are for the better. This is especially true with regard to the adoption of data tools, and in particular the incorporation of automation, which an employee may interpret as a threat to their place in the organization. It is therefore important to make sure you present the new tools to your employees, explaining to them how they make their work easier, direct them to carry out their own analysis and draw higher-level conclusions, and actually make them better employees, with improved performance and greater value to the organization.

What’s more, it’s important to remember that the system cannot answer all the questions and needs that arise in the organization. Therefore, it is important to implement the system with the same people for whom it is intended. There’s no point telling an employee about the wonderful capabilities of the system if those capabilities do not help them perform their job. On the other hand, when the system is left in the hands of the right employees, those who already know how to change, design and derive from it reports, conclusions and great benefit, and the more they do so, the more the system will change from a foe into a friend that supports their work routine.


4) Nowadays, you can achieve wonders with advanced analytics technologies and artificial intelligence but, in the end, everything stands or falls depending on the quality of the data

The ability to properly analyze and process huge amounts of data in a short time is what differentiates successful organizations from companies that drag their feet and ultimately fail. Along with more traditional data analytics tools, you can use innovative artificial intelligence technologies to build systems and applications that will miraculously improve the work of your organization, provide a better experience for customers, and drive sales and profits. Today there is almost no limit to what can be achieved using advanced technology. For example, you can build a personalization system and offer customers customized products, or train a smart system to direct customer inquiries arriving into an overflowing inbox to the relevant parties in the organization.

However, all of these wonderful things largely depend on the proper management of the data and data channels. Most companies have a large amount of data stored in databases, ERP systems, operating systems, Excel files and more. Data overload creates a demand for resources, data storage and management that would otherwise not be needed, so it is important to prioritize data according to the quality of the conclusions that can be drawn from it. In addition, it is important to make sure that your data is reliable, relatively accurate, and good quality (for example, that there are no multiple missing values), and to optimize it as necessary. Bear in mind that quality data is the fuel of data analysis and artificial intelligence systems.


5) Make sure your user interface is simple and clear. Overloading the data in the visual display may produce noise instead of a clear image that enables decision making

Finally, the appearance of the data in your user interface is critical. Visually presenting data and information in a simple and clean way, so that it is easy to look at, understand and draw conclusions from, is crucial to the effectiveness of using the system.Make sure texts are uniform, short and clear, and that graphs are simple and easy to understand. Remember that this is the face of the system, and it doesn’t matter who the users are – whether they are your employees, your customers, or both – it is important that it looks good.


There’s no shortage of data, but the challenge lies in knowing what to do with it. Following the tips above will set you on track to harnessing the value in your data. Once you get that right, you’ll soon be reaping the rewards.

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