In computer science, the saying "Garbage in, garbage out (GIGO)" is used to express that poor quality data produces poor quality results

Garbage In, Garbage Out & How to Avoid It: Data-Driven Transformation

By now, nearly all businesses have embraced data-driven transformation processeses. However, as the IT expression "Garbage in, garbage out" implies, many organisations are jumping on the data-driven transformation boat without knowing where they are going, leading to poor results.

data-driven transformation y como convertirse en compañía data-driven kale

Nowadays, and especially in the business world, investing in digital transformation and leveraging data to drive growth, generate business opportunities and offer better services and products to customers has become essential. 

Data-driven transformation or is becoming one of the preferred ways for companies to make the transition to the new digital era. 

However, contrary to what many organisations mistakenly assume, initiating a data-driven transformation process does not simply mean producing and storing data. It is not even about having a relatively large digital presence.

Data-driven transformation goes well beyond and, through a long but steady journey, it must support the alignment of analytic initiatives with business objectives, focusing on quality data and an established data-driven culture. Only then will data's potential be harnessed, and we will achieve good results. 

This is precisely why, despite the fact that we are generating more data than ever before, we are not making the most of it. A research conducted by NewVantage Partners confirms that 62.2% of businesses have not yet succeeded in establishing a data-driven culture. Forbes published a survey in which most CEOs stated that their companies are becoming less and less data-driven.

The problem with implementing data-driven transformations in business lies, to a large extent, in the fact that companies expect low-quality data to produce great results. As the IT expression "Garbage in, Garbage out" puts it, for data to generate value, it is essential that the data is of quality and contains value.

But what is valuable data?

Data by itself lacks value and, as we explained in the article "What is the difference between information and insights", data is not information and information is not the same as insights. To transform data into valuable information that provides productive knowledge when making business decisions, the first step is to ensure its quality, validate it, consolidate it, process it and analyse it. In short, and making a comparison with the recycling industry, transforming waste into material

 

Why data-driven transformation?

Management consultancy OliverWyman claims that leveraging data analytics and data-driven transformation is a game-changer for companies and puts them at an advantage over their competitors. 

Specifically, according to the US firm, data-driven transformation entails:

  • Risk reduction: Quality data and a good analytics strategy aligned with business logic can culminate in predictive analytics. Predictive analytics increase the accuracy of customer-centricity and allow organisations to stay ahead of future market trends, as well as reduce risks.
  • Provide a better offer and deeper connections with customers: Data is the main asset to get to know customers which allows us to obtain a more comprehensive view of their needs. Based on knowledge and through segmentation strategies, for example, we can create personalised offers and optimise our customer experience.
  • Greater efficiency and speed: The availability of automated analytical tools that generate insights in real time allows managers to make decisions almost instantly.
  • Increased revenue: As mentioned above, the optimisation of processes and operations and, ultimately, the creation of more solid, lasting and binding relationships with the customer leads to higher profits for the company.
How to become a data-driven company and avoid "Garbage in, Garbage out"?

Luckily, in this case, there is no need to take risks. 

Kale helps companies to drive their data-driven transformation, considering each business' specific needs. We study companies' position in terms of customer data-driven processes, identify development paths and implement the necessary measures to generate a data-driven culture focused on the customer and adapted to an increasingly competitive and omnichannel digital environment. 

In fact, both for businesses that want to start their transformation from scratch and for those that want to reinvent their data-driven strategy, there are certain requirements that any data-driven company should meet:

1. A consolidated data-driven culture. For a company to be truly data-driven, it is essential that the commitment towards a proper treatment and usage of the data is present at all levels within the organisation. All employees must align their efforts to this end and the role of data must be embodied in the business strategy. 

2. Prioritise the use of data in line with business objectives. These objectives should drive the use of data for decision making. The collection, processing and use of data must match the business needs.

3. Data must help place the customer at the centre of the organisation and must serve as a system for knowing clients better and improving the customer experience.  At Kale we specialise in carrying out data-driven transformation processes focusing on the customer.

4. Ensure data governance. In addition to making data available, data must be reliable, of high quality and consistent with data consumers' business needs. 

5. Use data to evaluate and optimise actions. In addition to serving as action drivers, data should also be exploited to evaluate, gauge and optimise the actions and strategies carried out. 

In today's digital ecosystem, data-driven transformation is an inevitable requirement that, sooner or later, all companies will have to embrace. However, in order for it to be efficient and beneficial, companies must focus their efforts on creating a comprehensive data-driven strategy that is present in all business areas, as well as ensuring the quality, governance and business logic of their data.

Would you like to start a data-driven transformation process? We can help you!

 

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