Only report data analysts have a future?

First of all, the conclusion, to make a report is the data analyst's side trick, but not the basis of standing up.

Starting with personal abilities, reporting is a common competency in the workplace and a must-have for data analysts. whether it's an operational, financial, or data analyst, making a statement is a no-no. but only making statements must be very low ceiling, doing reports is largely an executive class of things, you see how many executives will do the report themselves? it's all about arranging grass-roots employees to do it, so just making statements doesn't support your long-term growth in the workplace, especially for data analysts, business understanding and sensitivity to data is the core competencies of business data analysts.

besides, the application level of enterprise report, only do the report sooner or later will be eliminated, after all, more people rely on professional reporting tools, greatly reduce the difficulty and cost of report production, development, traditional report analysis has been difficult to meet the requirements of the enterprise for data development.

from the point of view of data analysis process, including data collection, data processing, data display, data analysis, and most of the reporting work is basically reduced to the material and source of data analysis, the real data analysis is the use of a variety of data analysis methods and models to report data and research, through data analysis to find the internal relationship and law of data. that is, analysis is an ongoing iteration of a series of behaviors that explore value from data, and the report's role is only to aid the process.

From the point of view of enterprise development needs, the main body of enterprise data analysis in the era of big data has developed from the original decision maker to the traditional reporting system established later, to the present business intelligence BI system, artificial intelligence, etc., and the traditional report can not meet the needs of enterprises to establish an efficient and reliable data processing platform. Simply put, enterprise data value mining requires not only the support of reporting systems, but also deeper data platform systems, such as FineBI.

data report analysis, only report data analysts have a future?

From the point of view of data analysis system, the architecture includes data storage layer, data report layer, data analysis layer and data presentation layer from bottom to top. In today's market, more businesses are choosing to rely on reporting tool systems to build architectures, such as FineReport, a report with data analytics ideas that cover the capabilities of early business intelligence. Therefore, the advanced nature of reporting tools also promotes the expansion of the field of data analysis, if only in the report work, will definitely be replaced.

data report analysis, only report data analysts have a future?

data report analysis, when doing data reporting, which types of data suitable for what graph analysis?
i think as long as focus on the commonly used a few charts would be good, not so complex, focus on data processing, the year of the sultry year

The following illustration gives an example of fineReport, a reporting tool.

bar chart:

suitable for two-dimensional data sets that show changes in data over time or describe comparisons between items. classified items are organized horizontally, numerically vertically, to emphasize changes in data over time or other conditions, and to apply small to medium-sized data sets.



line chart:

line charts are suitable for large two-dimensional data sets, especially where trends are more important than a single data point. assuming you need to see the total contract contract amount movement for each year, it is most appropriate to select the line chart component to provide data analysis.



pie chart:

pie chart i think is a chart that should be avoided, because the naked eye is not sensitive to area size. however, when a specific proportion is reflected, with a specific value, there will be better results.



scatter plot:

scatter plots are suitable for 3d datasets, but only two dimensions need to be compared.



Bubble chart:

Bubble charts are derivatives of scatterplots that reflect third dimensions, such as the cross quadrant bubble chart, by the area size of each point.



Radar:

Radar charts are suitable for multidimensional data (more than four dimensions) and must be sortable for each dimension. data points are generally about 6, too many words can be difficult to identify.




Data map


closely related to geographical location, you want to know the distribution of regions can choose data maps



these charts are simple to use, but the most common color matching, size, lines are very careful!



Column chart

The column charts are versatile and are good at showing how data changes over time, compares different data, and relationships between parts and wholes.

vertical column charts are used to show time-ordered data. stacked column charts can compare the relationship between parts and the whole, can be applied to discrete and persistent data, and can be stacked horizontally vertically.

horizontal bar charts can be used when data classification labels are long. when the total value of each group is not very important, only a partial-whole relationship can be considered, with 100% stacking.

make notes:

1, try to use horizontal arrangement of text labels

2, column bar spacing should be appropriate

3, Y AXIS VALUES START FROM 0

4, color matching consistent and harmonious

5, data arrangement should be reasonable

Pie chart

pie charts are easy to express data parts and overall relationships and are suitable for discrete and persistent data. when the amount of data is small, this method is the most attractive and easy to understand.

make notes:

1, pie chart classification is best not more than 5 kinds, the variety of multi-percentage is difficult to distinguish

2, do not use multiple pie charts as a display of data comparison relationship, such a comparison relationship bar stacking is more appropriate

3. make sure that all data percentages add up to 100%



Area map

area charts can represent time series relationships for data, and unlike line charts, area charts can clearly represent the amount of emission

stacked charts, for example, can be used to visualize the relationship between the presentation section and the whole, and to show the contribution of the portion to the total.

make notes:

1, do not use area chart to show discrete data, as far as possible to show stable changes in data

2, do not show more than 4 sets of data classification, too many data classification will make the chart appear complicated, difficult to read

3, to design easy to understand, the change of large data on the top, the amount of change of small data below

4, flexible use of transparent color, try to ensure that the data do not overlap, if it is unavoidable, you can use transparent color



Thermal map


Hotspot maps can display classified data and use a strong sense of color contrast to represent geographic regions or data lists.

Make notes:

1, the use of simple map outline, should not be too sharp

2, pattern use too much, increase style changes, easy to let the reader confused

3, choose the right data range, data range selection should be flexible, 3-5 groups of range can be

4, color selection to be appropriate, the use of series of colors more in line with the current public aesthetic



The data report analysis, what do you think of the data visualization industry and bi report development prospects?



data visualization is not a new technology, but with the development of the internet, data visualization is also evolving, especially with the rapid development of big data, big data-based visual analysis is also more and more attention, through the establishment of data warehouse to achieve the integration of enterprise multi-source data, and based on data mining, machine learning and other related technologies, mining the potential value of data, for enterprise operational decision-making, strategic analysis to provide data support, so the future data visualization still has a great prospect for development, especially based on big data, internet of things and other technologies, data acquisition processing as the core, interactive data visualization will be welcomed in all walks of life.

Data report is an important data management means when the enterprise operates, many enterprises are using excel to establish data report, but with the development of data analysis, data visualization, business bi and other means, report development gradually turned to platform, through the platform configuration to quickly generate data reports, and realize real-time interaction between online and offline. at the same time, through the establishment of enterprise data warehouse, data visual analysis and data report combination, so that data report more flexible and diverse, closer to the enterprise business.




Data visualization and data reporting are not new technologies, but with the development of the internet, are rapidly evolving, through the integration of big data, internet of things and other means to upgrade, so as to provide more comprehensive support in business operations, management, decision-making, analysis, so their development prospects are relatively broad.