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Every company has hundreds of millions of records about their sales, expenses, employees and so on, with dozens of insights yet to be discovered through simple comparison or triangulation of relevant data.

Why don’t we focus on this? I think because it’s very difficult to do – being successful in this “data translator” role requires a unique set of skills and knowledge, the combination of which I call the BASE skillset: Business understanding Ability to synthetize and simplify Storytelling skills Expertise in data visualization

Business Understanding This one seems obvious, but it doesn’t mean simply understanding the financials of a business. Rather, it means truly knowing the operational details, the incentives, the install base, market growth, penetration, the competition, etc. An analyst can’t just know the technical aspect of a report or the math behind the numbers, but what is truly driving a pattern in terms of product quality, competition, incentives and/or offerings. The best analysts are able to mathematically isolate the key levers of a trend and then suggest actions to react to or take advantage of those trends. Ability to Synthetize and Simplify This is, in my opinion, the most underrated and underappreciated skill. Combing through thousands of data points and netting out 3-4 key issues in under 10 minutes, and then communicating these to a group of execs with very different analytical skills, is truly difficult. The key is to make it simple but not simplistic, which means you still capture the complexity even as you get to the few core insights. It requires a very thorough effort to gather all the relevant information before categorizing, prioritizing and deciding if it is significant. After a while, you become an expert and can sniff things out quickly. At the same time, there is the danger of missing anomalies when you jump to conclusions based only on a summary look.

Storytelling Skills There are stages that should be followed when explaining complex ideas, something data translators are frequently expected to do. The best storytellers start by giving context and trying to couple the current discussion to something the audience already knows, ensuring the story is well structured and connected. We have to move from a “buffet style” business review with thousands of numbers packed in tables to a layered approach that will guide the audience to focus first on the most relevant messages, diving deeper only when necessary. Minto Pyramid Principles, which are built around a process for organizing thought and communication, are helpful in making sure you really focus on what is important and relevant, versus being obsessed in telling every fact. Expertise in Data Visualization I am glad to finally see so much focus on Information Visualization and I believe this is correlated to the explosion of data. Traditional methods of organizing data do not facilitate an intuitive understanding of key information points or trends. For instance, the two examples below contain data on car sales across the U.S. The first, an alphabetized list, is much less intuitive than the second, which shows those sales on a map in Power View. With Power View, right away you can identify the states with the highest sales: CA, FL, TX, NY. (Workbook available here)

There is no better way to see patterns or trends than data visualization, making expertise in this area – both technical and analytical – critical for data translators.

Most Data Professionals work in teams of up to five people. “Companies are starting to realize that Data is key to their success. The majority of them, though are not growing their Data Science teams fast enough to win. This maybe because they don’t want to or because they can’t. This is an alarming trend though and only software can come to the rescue,” noted Aziza.

Unul din principiile de influenta ale lui Robert Cialdini este dovada sociala. Acesta spune ca atunci cand ne aflam in fata unei decizii, avem tendita de a-i imita pe ceilalti.

recomandari de produse populare: in functie de categoriile de produse pe care le-ai cautat, Emag iti va recomanda si produse similare folosind un titlu care iti arata ca si alte persoane au fost interesate de acele produse.

comentarii si feedback: daca arunci un ochi peste produsele disponibile pe Emag, vei vedea ca majoritatea au comentarii, review-uri reale de la cumparatori. Astfel de comentarii vor influenta decizia de cumparare a celor nehotarati.

De exemplu, Emag trimite un email catre clienti la cateva zile dupa achizitie.

3. Optimizeaza-ti magazinul online pentru mobil

Chiar si Google a observat acest trend, asa ca la inceputul acestui an a atras atentia ca odata cu aparitia noilor algoritmi de indexare, paginile optimizate pentru mobil vor avea doar de castigat.

Verifica aici daca magazinul tau online este optimizat pentru mobil.

4. Personalizeaza pagina de eroare 404

Pentru a nu pierde un potential client, personalizeaza pagina de eroare 404 adaugand 2 elemente: Un titlu care sa explice ce s-a intamplat (De exemplu, „Pagina cautata nu a fost gasita. Se pare ca ai accesat un link expirat sau gresit.”) Un indemn la actiune (De exemplu, „Click aici sa te intorci la pagina anterioara”)

5. Foloseste efectul amortizorului social Un studiu psihologic realizat in 1972 a demonstrat ca daca stii ca exista cineva cu care poti vorbi, care iti va oferi ajutor in situatiile stresante, aceste momente vor fi mai usor de suportat.

Fii pe faza ca sa le raspunzi vizitatorilor la emailuri, la mesajele de pe social media Afiseaza in mod vizibil pe site datele tale de contact Implementeaza un live chat (iti recomand www.purechat.com – e usor de folosit si are si o versiune gratuita)

The potential benefits of business intelligence programs include accelerating and improving decision making; optimizing internal business processes; increasing operational efficiency; driving new revenues; and gaining competitive advantages over business rivals. BI systems can also help companies identify market trends and spot business problems that need to be addressed.

BI data can include historical information, as well as new data gathered from source systems as it is generated, enabling BI analysis to support both strategic and tactical decision-making processes.

BI programs can also incorporate forms of advanced analytics, such as data mining, predictive analytics, text mining, statistical analysis and big data analytics.

In many cases though, advanced analytics projects are conducted and managed by separate teams of data scientists, statisticians, predictive modelers and other skilled analytics professionals, while BI teams oversee more straightforward querying and analysis of business data.

Business intelligence data typically is stored in a data warehouse or smaller data marts that hold subsets of a company's information. In addition, Hadoop systems are increasingly being used within BI architectures as repositories or landing pads for BI and analytics data, especially for unstructured data, log files, sensor data and other types of big data. Before it's used in BI applications, raw data from different source systems must be integrated, consolidated and cleansed using data integration and data quality tools to ensure that users are analyzing accurate and consistent information.

In addition to BI managers, business intelligence teams generally include a mix of BI architects, BI developers, business analysts and data management professionals; business users often are also included to represent the business side and make sure its needs are met in the BI development process.

To help with that, a growing number of organizations are replacing traditional waterfall development with Agile BI and data warehousing approaches that use Agile software development techniques to break up BI projects into small chunks and deliver new functionality to end users on an incremental and iterative basis.

consultant Howard Dresner is credited with first proposing it in 1989 as an umbrella category for applying data analysis techniques to support business decision-making processes.

Business intelligence is sometimes used interchangeably with business analytics; in other cases, business analytics is used either more narrowly to refer to advanced data analytics or more broadly to include both BI and advanced analytics.