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Empowering data decisions

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Empowering Data decisions

The reality - at the end of 2022 - reflects the fact that businesses are unable to fully exploit the potential of their data. C-Executives and Analytical teams are plagued by labor-intensive analytical processes, that require a high labor load, time, and attention. This leads to not following up with the market timelines and losing the opportunity and momentum. Individual resilience or data constraints are factors that lead to failure in reaching meta targets, leaving the ability to fully understand the data and business intelligence, reduced due to time constraints.

Why this happens?

Everyone seems to be an expert. The market has evolved during the last 10 years, and the emergence of Data Analysts, Data Science, and BI specialists has increased to reduce significantly demand. Nevertheless, to move forward, data engineers and business intelligence analysts need to look into their data differently. To reach targets and be possible for the organization to dream with a quantum leap status, a path to find the Insights that matter needs time to be fully explored.

80% Sales Agents mention on their scripts they are experts in Data.

63.5% they mention they work with Big names / have relevant projects in house / have a huge competence team.

51.24% sends a proposal not so relevant, or the deliverables never meet the expectaction or market need.

Where and when does the problem start?

In nowadays market, a project team is made of analysts, who since school have been trained to produce dashboards, reflecting past data or some future forecasting translated into KPIs (Key Performance Indicators). After the first collection, the same analysts take this data to the project manager or even to the senior consultant, who based on experience assumes a set of different scenarios and compares the data against it.

This has been a system for the past 50 years that worked quite well, but the same ended up being overused and outdated. The true challenges of an internal or outsourced strategy team, should not be to provide these fancy dashboards and powerpoints but to have the market knowledge and understand the data of the organization, to fix operational problems, speed up product development cycles, optimize supply chains and bring up progress to the organization faster and in a constant pace.

1.

Newly graduates. - To fill up the demand, many Data Analysts are recent graduates or still students and the know-how of how to deal with data is still not developed compared to a senior data analyst.

2.

Lack of market maturity. Since most strategy teams are made of young people, the same do not have yet the market maturity and the market knowledge required to produce extraordinary insights.

3.

Lack of Imagination. Studies show that the quality of strategy teams has decreased by 58% in the last 15 years. It is more common practice to rather analyze and reaply a concept that was used before, other than inventing completely new models.


The problem of Models

Company A produces woman clothes. The supply chain is quite simple. From the three manufacturing warehouses, individual trucks would leave every day and restock the stores spread across the whole country. To reduce costs, the strategy and BI team assumed that the zip code of that country made of 5 digits, would not vary too much if the stores would be bundled by zip code, and in each town place a distribution center that would control the stock and restock the stores accordingly. This model in theory would spare fuel and reduce fleet costs. The mistake was, that the zip codes, would be spread through the whole county, producing even more costs rather than the initial plan.

This example demonstrates that market knowledge is indeed necessary for any data analyst, to not fall into the fallacy of looking purely at numbers. Even if the model would work for other countries that follow standardised practices, the same was ineffective in this case.

38% of junior data analysts, have not been through business school.

29% of data analysts does not look at the research when analysing data.

Empowering Business Teams with Improved Analytics Processes

Companies have a well of data, ranging from customer feedback surveys and in-app reviews to financial data and even vendors' lead times and their pricing fluctuation. However, the same data rarely enters the accounting books as an asset. There are many reasons for this, among others, the organization does not understand the concept of getting value from data or it is just impossible to capitalize the value of the data.

The ones who do understand, have a clear advantage in the market, by leveraging all the information. This data, which may include business transactions and customer information, is derived from spreadsheets or structured query language (SQL). Its embedded relationships between different tables and spreadsheets, make it simple to store, process, access and extract important KPIs. In the market, there is numerous software available that can produce these models to have almost near real-time KPIs and Business Intelligence Insights, but it is way harder than traditional analytics or predictive analytics—there is usually some data wrangling required before it can be analyzed. The models are far more complex, and they are difficult for data scientists to interpret—they have many, many variables or features, and none of them have any real-world referent.

"There is a general difficulty and opportunity
in capitalizing data as an asset."

Some businesses see the latent value of data. For example, in the coming year, the percentages of respondents who say their organizations are prioritizing further AI techniques (32%) and data infrastructure improvements (32%) are comparable. It would be tremendously helpful, for example, if an insurance provider could estimate the cost of repairing your car based on images of the collision damage. We couldn't do it until very recently. AI methods are currently being used in several ways to extract insights from unstructured data. For example, natural language processing is used to extract meaning from data sources such as emails, documents, and others, whereas pattern recognition algorithms are used to detect visual information. Data has a tremendous lot of value, and as technologies advance, it will be a major area of concentration for corporations.

Choosing the right technology and team

To find the market expertise, it is norm for different organisations to try out different consultants until they reach a match. For example, a pitch of some of our activity in 2022:

· Increased the database of an online clothe retailer
from 223 Customer up to 5091 Customer, resulting in an increase of 44.26% more sales and revenue.
· Reduced in 59% the 5 digits weekly logistics costs for a medical sample logistics operator.
· Modelled a system that increase the detection of fraude in casinos by 21%,
producing the retention of more than $10M in non-declared cash retention.
· Got the first Governement contract producing intelligence for the military.

However nortb data and BI might not be the right match to your organisation. Our methodologies are based on speed and leverage of specific market expertise within specific segments and thinking out of the box might not always work has it has been working for our clients so far.

In any case, if you decide to give us a shot, contact us in the form below!

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