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Redefine Analytics with Advanata

1–2 minutes

Can Advanata truly change how analytics are done? Can it stand out in a crowded market? Can it actually deliver better results? Is it too complex for users? Is it too simplistic for real problems? All are valid questions, so let’s start from the very beginning.

Why Should Customers Use Data Analytics?

Customers may question using analytics to help solve their business challenges. Proper utilization is essential to efficiently achieving goals and customers are at a disadvantage if they aren’t leveraging analytics.

Why should a customer use data analytics? They should be used in order to study the past. Why? In order to forecast the future. Why? In order to act accordingly. Why? In order to achieve your goals. All of this is in order to achieve your goals efficiently. Advanata goes one step further and allows for the optimal achievement of your goals. Analytics are essential to success.

Types of Analytics Customers

All customers need to be using analytics to overcome their business challenges and all of them can benefit from using Advanata.

Three types of analytics customers exist, and they will all benefit from using Advanata. Prospective customers are curious about using analytics to solve business challenges for the first time. Perplexed customers tried analytics before but found the experience and results lacking. progressive customers use analytics successfully but want faster better results with less effort.

Analytics Are Overly Complex

Analytics complexity is a common complaint and is often worsened by poor analyst communications. The fact is that the business problem framework appears again and again in real world analytics applications and once identified it can be easily reduced to its basic components.

Customers claim that analytics are complex and they don't know what to do or where to start. Advanata uses the business problem framework (consisting of goal(s), options, and resource) to reduce complexity. This solves the majority of real-world problems, requires minimal data and less analytics work, and are easy for all parties to use and understand. Advanata provides an easy to use and powerful analytic framework.

Customer Involvement is Restricted

Excluding customers is a major analytics process flaw. Customers are essential and offer a wealth of knowledge that saves time and effort. Using the inductive approach properly coordinates between all parties and maximizes their contribution to solving the analytics problem.

Customers say that they understand their business best and want a key role in the analysis process. Advanata uses the inductive approach (utilizing human intelligence, data intelligence, and artificial intelligence) to optimize participation of each party. This approach is customer driven to ensure confidence, captures qualitative domain expertise, and maximizes benefit from each source. Advanata organizes and maximizes utilization of each knowledge source.

Actionable Results Are Needed

Vague results are a recurring analytics issue. Customers are not analysts; they need explicit results and not additional analysis work in the form of insights or recommendations. The data effectiveness methodology ensures precise actionable results that require no further interpretation.

Customers need results that they can use, they don't want more analysis work. Advanata utilizes the data effectiveness methodology (outputting precise actions) in order to increase analytics utilization. Therefore, there is no need for follow-up analysis work, no interpretation or execution risk, and entirely based on customer defined goals. Advanata outputs precise optimal actions to reach desired goals.

Advanata Can Change How You Do Analytics

Effective use of analytics is essential for any customer aiming to succeed in today’s business environment. While adoption may present challenges, the benefits it unlocks are too important to ignore. Advanata was designed from the ground-up to be a transformative analytics tool by taking a unique customer-led approach, targeting actual real-world analytics applications, and generating precise usable outputs.

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