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When it comes to medical and health big data, it is easy to think of clinical big data, health big data or biological big data
.
Taking clinical big data as an example, the acquisition of such data has a high threshold, the data itself has a high value, and whoever owns the data has the initiative to
develop related applications.
There is also a type of data, which has a lower threshold for acquisition, and after the combing and integration of professional manufacturers, it has also begun to show unique value in many scenarios, and has begun to be valued by many pharmaceutical companies to solve various problems
faced in their own business processes.
It is the big data of
medical circulation.
Scattered and chaotic medical circulation data
Scattered and chaotic medical circulation dataThe reason why big data of pharmaceutical circulation was not paid attention to in the past has a lot to do
with the business chain of pharmaceutical circulation.
Pharmaceutical distribution solves the problem
of connecting pharmaceutical manufacturers, terminals of sale and consumers.
The main business models can be simply divided into three categories
: wholesale mode, retail model and new circulation model.
The wholesale mode alone can be divided into hospital direct sales, commercial transfer, and third terminal
according to different sales objects.
Coupled with pharmaceutical logistics, the links involved are quite extensive
.
If there are more links, the more
data will naturally be generated.
Simply pinch it, just from the dimension of medical agents, you can get data of large categories such as flow analysis, competitive product analysis, core hospital tracking, and license management
.
Data that can be generated from the perspective of medical agents
One of the subdivisions, such as flow direction analysis, can also be divided into data
such as drug regional flow, drug terminal flow, drug sales, drug sales proportion, terminal contribution ranking, terminal contribution ratio, medical representative contribution, and medical representative performance ratio.
Competitive product analysis can give data
from the perspective of product to the market share, annual sales comparison, monthly sales trend, terminal sales analysis, sales status early warning and other perspectives of competitive manufacturers.
If necessary, the granularity of the data can be further refined, such as tracking the core hospital, not only can you view the sales status of all hospitals from a global perspective, but also see the change in the number of hospital terminals, which are the core partners, which are the maintenance status, which need to be promoted, and which are in the "dormant" state
.
It can also conduct in-depth analysis for individual hospitals, drug sales ranking, drug loss, drop status, transaction time, past order frequency, amount, etc
.
In addition to medical agents, various data
can be generated from multiple dimensions such as logistics management, medical representatives, and funds.
Data generated from the perspective of a medical representative
The pharmaceutical circulation link itself is a nonlinear multi-level network structure, so the data it generates is also scattered and chaotic
.
This data has always existed, but it has not been as valuable
as it should be.
Taking the data generated by the dimension of medical representatives as an example, in the past, it was mainly used for the performance evaluation of the sales department, but many times the sales data was filled in by the medical representatives themselves, and some even the sales expenses were calculated
on their own behalf.
Leaving aside whether such data is valuable, it is not desirable
from a compliance perspective alone.
At best, such data is used within the sales department and does not have the value to connect with the
entire company.
The rest of the data is also in this situation, whether it is logistics and transportation, procurement management or capital analysis, the data they generate can only partially reflect the situation of the department, and there is no talk about the application of
big data.
Gather sand into a tower to maximize the value of data
Gather sand into a tower to maximize the value of dataAlthough there are some problems with data in the field of pharmaceutical circulation, the value behind it should not be ignored.
With the implementation of policies such as the two-invoice system, consistency evaluation, volume procurement, and medical insurance fee control, the drug circulation link has been compressed and the drug price space has been squeezed
.
The era of high gross profit is gone, pharmaceutical companies are also facing the challenge of refined operations, and the value of big data is gradually recognized
by pharmaceutical companies.
Pharmaceutical companies should first solve several problems when using data, one is the collection of data in the entire circulation process; The second is to clean and summarize the collected data; The third is to form a report
from data.
Of course, pharmaceutical companies cannot do these things themselves, and professional channel digital solution providers
are needed.
Suppliers need to build a bridge between pharmaceutical companies and the distribution link to undertake the work
of connecting data.
It doesn't sound difficult, but in fact it is a tedious thing, and the enterprises in the drug circulation business chain have different business properties and different
types of data.
How do I connect my data? What is the approval process and time of the other party when the data is connected? What data fields are required? How often is the data updated? etc.
are all issues
to consider.
After docking, the data needs to be cleaned, classified, and managed before it can become valuable data
.
The master data management system provided by the supplier is used here, and its biggest role is to ensure the internal and external understanding of the enterprise, make it consistent, complete and controllable, and avoid information transmission errors caused by different data recording habits
.
Therefore, the master data management system for pharmaceutical enterprises will be the first to usher in an explosive demand
in the commercialization of pharmaceutical circulation big data.
The collected data is not just a summary of a single department, but can show the whole picture
of a business link in a more three-dimensional way.
For example, based on the standardized operation process of "preparation, execution, quality inspection, analysis and delivery", through cleaning, inventory, data backwards and comparison, accurate channel data of distributors and products are obtained, product flow and goods channeling management are efficiently tracked, rebates, replenishment and compensation quantities are confirmed in time, dealer inventory management problems are quickly identified, and the level of distribution data management is
improved.
If the entire pharmaceutical circulation link can generate big data reports in this way, it is undoubtedly a sharp tool
for enterprise management to review their own development.
"For enterprises, big data is to help them see the whole picture, understand themselves and the industry, compare their own situation with industry trends, and then make decisions
.
" Wang Dingqiang, channel strategy director of Beitong Pharmaceutical, said
to Arterial Network.
Based on data, pharmaceutical companies implement precise management
Based on data, pharmaceutical companies implement precise managementThe value of big data in pharmaceutical circulation is mainly reflected in cost reduction and efficiency improvement, business opportunity exploration and risk early warning
.
■ Reduce costs and increase efficiency
■ Reduce costs and increase efficiencyIn the process of development, a pharmaceutical company acquires several companies to expand its product pipeline, which is a common thing
in the development of enterprises.
However, acquisitions are only the first step, and it is not easy to achieve alignment between subsidiaries and between subsidiaries and groups
.
Unified management and resource sharing are often difficult to achieve
.
In the simplest example, there may be problems with using different data standards between each subsidiary
.
For the subsidiary itself, this is not a big problem and does not affect the company's operations
.
However, from the group's standpoint, the lack of data connectivity means "dark lights", and there is no way to assess whether cost reduction is needed as a whole, nor can it follow up
in time when business opportunities arise.
By opening up multiple systems and unifying the data caliber, the operation of each subsidiary company is timely and accurately summarized and presented to the management of the group, which greatly improves the operation and management efficiency
.
The Group's resources can be allocated
more efficiently.
At the same time, based on the massive data processing capabilities of the big data architecture, the report output time of each team has also been greatly reduced
.
Overall operational efficiency is improved
.
■ Business opportunity discovery
■ Business opportunity discoveryFor pharmaceutical companies, how to find increment is a key point
that cannot be ignored in their work.
From a strategic point of view, it can be divided into three steps: holding on to the stock market, tapping the potential value of end customers, and competing for market share of
competitors.
First of all, through the data of monitoring the lack of items and reductions, pay attention to the encroachment of competitors on the stock market, timely remind medical representatives and adjust sales strategies
.
In the hospitals that have entered, through the data to understand the departments that should be covered by drugs but not yet covered, timely arrange work to promote the doctors of these departments to understand the drugs and promote drug sales
.
Through the comparative analysis of the terminal coverage of this product and competing products, promote the drug to invade the market scope of competing products and increase the market share
of this product in this category of drugs.
Big data empowers business chains
To put it simply, even routine work such as academic communication, customer maintenance, and offline visits should be arranged with the support of big data in order to achieve better operational results
.
■ Risk early warning
■ Risk early warningThe management of the group can directly understand the operational risks of its subsidiaries through big data, and big data can provide risk early warning and assessment
from different dimensions.
For example, from the perspective of funds, whether the sales situation is normal and whether the category is abnormal
.
If the accounts receivable collection period is long and the inventory is large and varied, then there will be a high capital occupation in the entire chain, and the financial burden will be heavy
for pharmaceutical circulation enterprises with low gross margins.
Management can assess the risks against these early warnings, consider whether internal audits are needed, whether there are loopholes in their own systems, and how to improve
.
And all this without even needing management to look through the financial statements, which is the value of
big data.
In addition to finance, there is also logistics, drugs from end-to-end transportation involves multiple links, order progress control, logistics information tracking, order completion efficiency analysis, cargo location management, regional operation monitoring and other aspects to improve circulation efficiency, reduce inventory costs, reduce consumption in circulation links
.
In addition to internal combing, the extracted big data also has external forecasts, which can help enterprises see the existing laws of the market and predict future trends, and provide data support
for management decision-making.
For example, for channel risk management, the distribution of quality index between different provinces, and the comparison between the same industry, the channel grid is purposefully optimized, and the supply rules and circulation rules
are adjusted in time.
Big data can also show its distribution of sales index on the traditional three terminals for a specific product, whether it meets the law of competing products of the same type, how should enterprises adjust sales strategies, so that enterprises can combine their own resources, product conditions, and more effectively use various ways to achieve business goals
.
Nowadays, the competition of pharmaceutical companies on the channel side has become increasingly fierce, and pharmaceutical circulation data, as the most direct response to business results, is an important reference for
pharmaceutical companies to grasp the sales performance of drug channels in a timely manner.
Pharmaceutical companies make marketing layout and sales management decisions, which are inseparable from the support
of big data in pharmaceutical circulation.
In this application scenario, big data of pharmaceutical circulation has firmly occupied the "C position"
.
Commercial applications still need regulatory support
Commercial applications still need regulatory supportWith the popularization of mobile Internet and 5G technology, all walks of life are inseparable from the application of data, and the country is paying more and more attention to
data security.
At present, China's pharmaceutical circulation industry is developing rapidly, the number and scale are quite large, and the amount of data generated is also astronomical
.
How to manage is an urgent problem
to be solved in the current medical circulation big data industry.
According to incomplete statistics, there are currently as many as 8 relevant national standards, many places have also introduced their own local regulations, and there are many management standards
within the industry.
Because it is not a mandatory standard, it is selectively enforced
.
There is no unified management standard for data, then there must be obstacles in the exchange of data, and these problems will eventually lead to bias
in the perception of data in the market.
From the perspective of industry development, it is necessary to further propose a standard to make the analysis and understanding of data more consistent and guide the business development of
enterprises.
Wang Dingqiang, channel strategy director of Beitong Pharmaceutical, told Artery.
com: "For data providers, because there is no standard, enterprises can only improve their own standards, judging from whether it involves state secrets, whether it involves commercial secrets, whether it involves personal privacy leakage, whether it involves unfair competition, and the legitimacy of data ownership, the legitimacy of data transactions and the legality of the transaction itself to ensure the compliant application
of data.
" ”
Thankfully, the industry has also noticed these chaos
.
The "Master Data Management Standard for Pharmaceutical Circulation Terminal Institutions" jointly compiled by 18 pharmaceutical circulation enterprises including the United Nations Pharmaceutical Association, including the China Pharmaceutical Commerce Association and 18 pharmaceutical circulation enterprises such as Shanghai Pharmaceutical and Beta Pharmaceutical, has been released
.
The standard is considered from the perspective of the most basic and core master data, and greatly solves the problem of
ununified upstream and downstream standard data interfaces.
If it can be implemented, it can break through the bottleneck of the basic work of informatization of operation and management of pharmaceutical circulation enterprises, so that the digital transformation of the entire industry can be at the same frequency as the development of the times, and achieve a win-win situation
for all parties.
Write at the end
Write at the endAt present, the main value of big data in pharmaceutical circulation lies in helping enterprises build their own digital capabilities
.
In the future, big data will also evaluate the whole life cycle of products, helping enterprises set business goals, market selection and corresponding pricing
.
In addition, big data can also provide guidance and suggestions in terms of market development mode, construction of assessment and assessment system, adjustment of organizational structure, optimization of management process and
system.
At the marketing level, personalized solutions are developed for specific customers, and insights are generated in academic and commercial inputs and outputs for enterprises to make decisions
.
In the future, big data in pharmaceutical circulation will appear in more scenarios and play a greater role
in assisting enterprises to improve the accuracy of decision-making.
