--- title: "6.7.1 Alternative data market: overview and size" description: "What alternative data is, who sells it and how much is spent: Neudata's figures reconstructed and the consultancies' widely differing estimates." url: "https://pythonparatrading.com/en/python-trading-course/level-6/alternative-data-market-overview/" lang: "en" image: "https://pythonparatrading.com/og/en/python-trading-course/level-6/alternative-data-market-overview.png" --- # 6.7.1 · Overview and size of the alternative data market > Alternative data is sold with market figures that range from less than 3 billion to more than 100 billion dollars depending on who publishes them. This lesson sorts out the types of data and the value chain, reconstructs Neudata's figures in code from its published method, normalizes the estimates of five consultancies and computes how much a dataset has to earn to pay for itself. - Course: [Level 6: Generative AI, LLMs, agents, big data and alternative data](https://pythonparatrading.com/en/python-trading-course/level-6/) - Module: 6.7 · Alternative data - Duration: 45 min - Prerequisites: [1.3.4 · Professional market data providers](https://pythonparatrading.com/en/python-trading-course/level-1/professional-market-data-providers/), [1.3.8 · How to choose a market data provider](https://pythonparatrading.com/en/python-trading-course/level-1/how-to-choose-a-data-provider/), [5.2.2 · Point-in-time fundamentals, macro and alternative data](https://pythonparatrading.com/en/python-trading-course/level-5/point-in-time-fundamentals-macro-alternative-data/) ## By the end of this lesson - Define alternative data by its origin (people, company processes, sensors) and place the course datasets in that classification. - Describe the alternative data value chain: source, vendor, catalog or aggregator, buyer and signal. - Reconstruct Neudata's spending figures in Python from its published method and detect which assumptions move them. - Normalize market estimates with different bases, horizons and rates to the same year, and measure their dispersion. - Compute the minimum alpha a dataset needs to cover its cost depending on the size of the portfolio. ## What alternative data is Lesson 5.2.2 defined it by exclusion: everything that is not prices, volumes or financial statements. It is a useful definition for a model, but it does not say where the data comes from or why anyone sells it. A more practical classification is by **origin**, because the origin determines the coverage, the delay and the biases (lesson 6.7.2) and also the legal risks (lessons 6.7.3 and 6.7.4): ```mermaid flowchart LR A[Alternative data] --> P[People] A --> E[Companies] A --> S[Sensors] P --> P1[Cards and receipts] P --> P2[Websites and reviews] E --> E1[Jobs and patents] E --> E2[Published prices] S --> S1[Satellite and AIS] S --> S2[Weather and location] ``` | Origin | Examples | What it measures well | Typical risk | |---|---|---|---| | People | Card transactions, email receipts, web and app traffic, reviews, social media | Consumer demand, almost in real time | Personal data, panel bias (who is in it) | | Company processes | Job postings, patents, catalog prices, public contracts | The company's plans and investment | Mapping to the right company, delayed publication | | Sensors | Satellite imagery, ship positions (AIS), weather, mobile geolocation | Physical activity: stores, ports, harvests | Cost, clouds and gaps, consent for geolocation | The level 6 data includes a synthetic example of almost every row, all of them about the 120 fictitious companies in the level 5 panel: cards (25 companies), web and app traffic (30), employment (60), reviews (10), ships (10 exporters), weather (12 utilities), parking lot images (8 chains) and patents (40). Each source measures part of the demand shock that moves sales relative to consensus, with its own noise and delay; lesson 6.7.2 goes through them one by one. ## The value chain Between the event (someone pays by card in a store) and a fund's signal there are several links, and each one charges: ```mermaid flowchart TB F[Source] --> V[Vendor] V --> C[Catalog] C --> B[Buyer] B --> S[Signal] ``` - **Source**: whoever generates the trace (a bank, an app, a satellite, a website). It often does not know that its trace has value for an investor. - **Vendor**: buys or collects the trace, cleans it, aggregates it, maps it to companies and sells it with a license (lesson 1.3.7). It is the one that bears the legal risk of the collection. - **Catalog or aggregator**: companies such as Neudata or Eagle Alpha maintain catalogs of datasets with profiles, indicative prices and evaluations; platforms such as Nasdaq Data Link distribute third-party datasets. Lesson 6.7.4 covers their role in *due diligence*. - **Buyer**: quantitative and discretionary funds, above all; also banks, insurers and companies that keep an eye on their competitors. - **Signal**: the transformation of the data into a prediction that can be traded (lesson 6.7.5). This chain explains why “market size” figures differ so much: you can measure what buyers of one type (investment managers) spend, what vendors earn from all their clients, or a “market” that includes data collected inside each company. Each choice gives a different figure. ## What Neudata measures and how For years Neudata has published an estimate of investment managers' spending on alternative data. Its figures can be read on its website[^1][^2][^3]: | Publication | Figure | |---|---| | 2025 report (press release of February 26, 2025) | Minimum spending of 2.5 billion dollars in 2024; average growth of 21% a year since 2020, 33% in 2024 | | 2025 report (method article) | Growth of 15% (2021), 13% (2022), 26% (2023) and 33% (2024); 2,338 datasets in the analysis | | 2025 report | Market of 3.3 to 15.4 billion in 2025 and of 8.6 to 39.9 billion in 2030 | | 2026 report (releases of February 23 and 24, 2026) | About 2.8 billion in 2025, 17% more than the previous year; 2,805 datasets; about 23.1 billion in 2030 “if current growth trends persist” | The **method** is described in the 2025 article[^1]: for each dataset in the catalog, the annual price given by the vendor times the number of investment manager clients it has. With three explicit assumptions: - If the vendor gives a **price range**, the midpoint is not used but rather the value at 30% of the range, `((max − min) × 0.3) + min`, because actual transactions are concentrated below the midpoint. - If the number of clients comes in **bands**, the midpoint is taken (from 6 to 10 clients, 8 buyers). - Only datasets with a full profile count. If the 5,600 short profiles (*Lite*) were added with the average spending of 1.6 million, the 2024 figure would go from 2.5 to 11.4 billion; and if the catalog were 90% of total spending, the 2024 range would go from 2.7 to 12.7 billion. Neudata calls it “a proxy for alternative data spending rather than a precise measure”, and warns that vendors may give it a list price that is later negotiated down and that “the margin for error is large”. With that description, the figures can be reconstructed: ```python import numpy as np import pandas as pd crec = pd.Series({2021: 0.15, 2022: 0.13, 2023: 0.26, 2024: 0.33, 2025: 0.17}, name="crecimiento") acum = (1 + crec.loc[2021:2024]).prod() media = acum ** (1 / 4) - 1 print(f"2021-2024: x{acum:.3f}, geometric mean {media:.1%}") base_2020 = 2.5 / acum print(f"implied spending in 2020: {base_2020:.2f} billion") nivel = base_2020 * (1 + crec).cumprod() print(nivel.round(2).to_dict()) ``` ```output 2021-2024: x2.178, geometric mean 21.5% implied spending in 2020: 1.15 billion {2021: 1.32, 2022: 1.49, 2023: 1.88, 2024: 2.5, 2025: 2.92} ``` The four growth rates given in the article match the 21% average announced in the press release. The 2020 spending they imply is not published by Neudata: it is a derived figure, and it has to be presented as such. The last row mixes two reports: the 17% for 2025 comes from the 2026 report, with a different sample of datasets, and applied to the 2.5 billion it gives 2.92 billion, not the 2.8 billion that report publishes (next section). *Fig. — Annual growth of investment managers' alternative data spend according to Neudata: 2021 to 2024 from the 2025 report (geometric mean 21.5%); 2025 from the 2026 report.* Bars: 15% in 2021, 13% in 2022, 26% in 2023, 33% in 2024 (highlighted) and 17% in 2025. A horizontal line marks the 2021 to 2024 geometric mean, 21.5%. The 2025 and 2030 ranges in the 2025 report are reconstructed the same way: the 2024 sensitivity range (from 2.7 to 12.7 billion) grown at 21% a year. The cell checks it: ```python r24 = np.array([2.7, 12.7]) # 2024, Neudata sensitivity g = 0.21 r25 = np.round(r24 * (1 + g), 1) r30 = np.round(r25 * (1 + g) ** 5, 1) print("2025 reconstructed:", r25, "published: [ 3.3 15.4]") print("2030 reconstructed:", r30, "published: [ 8.6 39.9]") print(f"from 2025 to 2030, x{1.21 ** 5:.2f}") ``` ```output 2025 reconstructed: [ 3.3 15.4] published: [ 3.3 15.4] 2030 reconstructed: [ 8.6 39.9] published: [ 8.6 39.9] from 2025 to 2030, x2.59 ``` The reconstruction matches what was published (2025 is the 2024 range times 1.21, rounded; 2030, the 2025 range times $1.21^5 = 2.59$). This says two things: the ranges are not independent forecasts, but a mechanical extrapolation of past growth; and the width of the range (from 1 to 4.7) does not come from uncertainty about the future, but from uncertainty about the present (what share of spending the catalog sees). ### The method, applied to a made-up catalog To see how much the assumptions move the result, the following cell applies Neudata's rules to a **made-up** catalog of 2,338 datasets (the number in the report), with price ranges and client bands generated with a fixed seed. The absolute figures mean nothing; the ratios between rules do: ```python rng = np.random.default_rng(671) n = 2338 minimo = rng.choice([25, 50, 100, 150, 250], n) * 1e3 maximo = minimo * rng.choice([2, 3, 4], n) tramos = np.array([(1, 5), (6, 10), (11, 25), (26, 50)]) t = tramos[rng.choice(4, n, p=[0.45, 0.25, 0.2, 0.1])] def total(precio, clientes): return (precio * clientes).sum() / 1e9 reglas = { "midpoint": total((minimo + maximo) / 2, t.mean(1)), "Neudata (30%)": total(minimo + 0.3 * (maximo - minimo), t.mean(1)), "minimums": total(minimo, t[:, 0]), } ref = reglas["Neudata (30%)"] for k, v in reglas.items(): print(f"{k:15s} {v:5.2f} billion ({v / ref:.2f}x)") ``` ```output midpoint 5.87 billion (1.25x) Neudata (30%) 4.71 billion (1.00x) minimums 1.87 billion (0.40x) ``` Switching from the midpoint to the 30% rule lowers the total by a fifth in this catalog; taking the minimum price and the minimum number of clients leaves it at 40% of Neudata's figure. None of the three is “the truth”: the truth is private contracts that nobody publishes. That is why a market figure without its method is not a data point, it is a claim. ## Revisions between reports The 2026 report gives 2.8 billion for 2025 with growth of 17%[^3]. The 2024 figure it implies is not the one in the previous report, and the 2030 figure does not follow from the rate it announces: ```python g25, gasto25, meta30 = 0.17, 2.8, 23.1 print(f"implied 2024: {gasto25 / (1 + g25):.2f} (published " "in 2025: at least 2.5)") tasa = (meta30 / gasto25) ** (1 / 5) - 1 print(f"annual rate from 2.8 to 23.1 in five years: {tasa:.1%}") for g in (0.17, 0.21): print(f"2030 at {g:.0%} a year from 2.8: " f"{gasto25 * (1 + g) ** 5:.1f}") print(f"2025 base that gives 23.1 at 21%: " f"{meta30 / 1.21 ** 5:.1f}") ``` ```output implied 2024: 2.39 (published in 2025: at least 2.5) annual rate from 2.8 to 23.1 in five years: 52.5% 2030 at 17% a year from 2.8: 6.1 2030 at 21% a year from 2.8: 7.3 2025 base that gives 23.1 at 21%: 8.9 ``` With 2.8 billion and 17% a year, 2030 would come to 6.1 billion; with 21%, to 7.3 billion. The 23.1 billion requires a rate of 52.5% a year from 2.8, or starting in 2025 from a base of 8.9 billion at 21%. The public pages of the report do not say what the base of that projection is (the 2025 report worked with a range of bases); the full report is downloaded after filling in a form. It is a question to ask the vendor before citing the figure, not a figure to repeat. The sample also changes: the 2025 article analyzes 2,338 datasets in 2024; the 2026 blog post speaks of 2,215 in 2024 and 2,805 in 2025. Comparing the spending in two reports means comparing two different catalogs. ## The consultancies: widely differing estimates Market research firms publish their own “alternative data markets”. The public part of each report (the press release or the product page) gives one or two figures, a base year, a compound annual rate and a period. Those of five firms, read on their websites on 2026-10-23[^4]: | Firm (date of the page) | What was published | |---|---| | Grand View Research (October 2024) | 135.72 billion USD in 2030; 63.4% a year from 2025 to 2030; *hedge funds*, 68.0% of 2024 revenue | | IMARC (updated 2026-10-05) | 12.0 billion in 2025, 16.2 billion in 2026 and 168.2 billion in 2034; 34.0% a year from 2026 to 2034 | | Technavio (January 2025) | Increase of 60.32 billion; 52.5% a year from 2024 to 2029 | | Precedence Research (updated 2026-09-17) | 14.16 billion in 2025, 21.61 billion in 2026 and 854.34 billion in 2035; 50.68% a year from 2026 to 2035 | | Arizton (published in November 2024, updated in February 2026) | 6.27 billion in 2023 and 79.22 billion in 2029; 52.61% a year from 2023 to 2029 | To compare them you have to bring them to the same years. With the rate and the period each firm gives, the missing values are filled in (the derived values, with their origin): ```python def compuesto(v, g, n): return v * (1 + g) ** n base_tn = 60.32 / (1.525 ** 5 - 1) # Technavio: 2024 base est = pd.DataFrame([ ("Neudata 2026", 2.8, 23.1), ("Neudata 2025 low", 3.3, 8.6), ("Neudata 2025 high", 15.4, 39.9), ("Grand View", 135.72 / 1.634 ** 5, 135.72), ("IMARC", 12.0, compuesto(16.2, 0.34, 4)), ("Technavio", base_tn * 1.525, np.nan), ("Precedence", 14.16, compuesto(21.61, 0.5068, 4)), ("Arizton", compuesto(6.27, 0.5261, 2), compuesto(6.27, 0.5261, 7)), ], columns=["firma", "y2025", "y2030"]).set_index("firma") est.round(1) ``` ```output y2025 y2030 firma Neudata 2026 2.8 23.1 Neudata 2025 low 3.3 8.6 Neudata 2025 high 15.4 39.9 Grand View 11.7 135.7 IMARC 12.0 52.2 Technavio 12.7 NaN Precedence 14.2 111.4 Arizton 14.6 120.9 ``` Grand View does not publish the 2025 value on the page (it comes from discounting its 2030 figure at its rate); the 2030 value for IMARC, Precedence and Arizton comes from applying their rate to their last published value; and Technavio gives an increase, not a level, so its 2024 base is solved from the rate (with its period from 2024 to 2029 there are five years of growth; the page itself also mentions 2025-2029, which would give another base). Before using any of these figures, it is worth checking whether they are consistent with themselves: ```python print("IMARC 2034 reconstructed:", round(compuesto(16.2, 0.34, 8), 1), "(published 168.2)") print("Precedence 2035 reconstructed:", round(compuesto(21.61, 0.5068, 9), 1), "(published 854.34)") print(f"Precedence, from 2025 to 2026: {21.61 / 14.16 - 1:.1%}") consult = est.drop(index=[i for i in est.index if i.startswith("Neudata")]) for col in ["y2025", "y2030"]: v = consult[col].dropna() nd = est.loc["Neudata 2026", col] print(col, f"consultancies from {v.min():.1f} to {v.max():.1f}", f"(x{v.max() / v.min():.1f});", f"versus Neudata 2026: x{v.min() / nd:.1f}" f" to x{v.max() / nd:.1f}") ``` ```output IMARC 2034 reconstructed: 168.4 (published 168.2) Precedence 2035 reconstructed: 865.3 (published 854.34) Precedence, from 2025 to 2026: 52.6% y2025 consultancies from 11.7 to 14.6 (x1.3); versus Neudata 2026: x4.2 to x5.2 y2030 consultancies from 52.2 to 135.7 (x2.6); versus Neudata 2026: x2.3 to x5.9 ``` The figure at the top plots the table: each row goes from 2025 to 2030 on a logarithmic scale, and the only highlighted point is the spending Neudata estimates for 2025. The five consultancies agree fairly well on 2025 (from 11.7 to 14.6 billion) and diverge in 2030 (from 52.2 to 135.7 billion), because their rates range from 34% to 63% a year. Three reasons explain the gap with Neudata: 1. **What they measure**. Neudata, the spending of investment managers on datasets in its catalog. The consultancies, a market with other buyers: their public segmentation includes banking and insurance, retail and other sectors, in addition to funds. Grand View attributes 68.0% of 2024 revenue to *hedge funds*; even so, its funds market would be several times the total spending of investment managers according to Neudata. 2. **How they measure it**. Neudata publishes its method and its assumptions. The consultancies' public pages do not: the method, if it exists in writing, is in the paid report. 3. **What rate they assume**. 50% a year multiplies the market by 7.6 in five years and by 58 in ten. The growth measured by Neudata between 2021 and 2025 was 13% to 33% a year. A sustained rate of 50% for a decade is an extraordinary hypothesis, and none of the pages justifies it. ## The figure that matters to the buyer For a fund, the size of the market is context. What decides is whether a specific dataset pays for itself. Neudata estimates that the average dataset in its catalog earned 1.1 million dollars a year from its investment manager clients in 2024[^1]; in addition to the price there is an internal cost of ingestion, cleaning, mapping to companies and research that the lesson cannot measure with public sources. The cell assumes 0.9 million (an assumption of the course, not a data point) and computes how much the dataset has to earn, in basis points on the portfolio, to cover both costs: ```python precio, interno = 1.1e6, 0.9e6 # internal: an assumption of the course cartera = pd.Series([0.1, 0.5, 1, 5, 20, 50], name="cartera_mm") aum = cartera.to_numpy() * 1e9 umbral = pd.DataFrame({ "solo_precio_pb": precio / aum * 1e4, "con_interno_pb": (precio + interno) / aum * 1e4, }, index=cartera) umbral.round(1) ``` ```output solo_precio_pb con_interno_pb cartera_mm 0.1 110.0 200.0 0.5 22.0 40.0 1.0 11.0 20.0 5.0 2.2 4.0 20.0 0.6 1.0 50.0 0.2 0.4 ``` *Fig. — Annual return a dataset needs to cover its cost by portfolio size: average price of USD 1.1 million (Neudata, 2024) and the same plus 0.9 million of internal cost (course assumption).* Two descending lines on log scales. With price and internal cost (2 million a year): 200 bp with a 100 million portfolio, 20 bp with 1 billion (highlighted), 1 bp with 20 billion and 0.4 bp with 50 billion. With the price only, 55% of those figures. A 100 million fund needs 200 bp a year just to cover the cost, something almost no dataset delivers to the whole portfolio. A 20 billion one needs 1 bp. This is the economy of scale that explains who buys: expensive data is concentrated in large portfolios and in strategies that use the same data in many positions. For an individual or a small fund, the realistic path is their own cheap data (public sources, responsible scraping, lesson 6.7.3) or datasets priced by usage. The other side of the calculation is **decay**: the more buyers a dataset has, the sooner its information is reflected in the price. Neudata reports that in 2025 the typical dataset had about 20 investment manager clients, versus 25 in 2024[^5], and interprets it as fragmentation of the market, not saturation. In the course data the decay is planted: the card signal loses almost all its value once its use is “published” in 2021 (`alt6_verdad_trimestral.csv`, column `phi`; lesson 6.7.5). ## Common mistakes - **Citing a market figure without a date or definition**. The 2025 figures range from 2.8 to 14.6 billion depending on who measures what. Without a definition, it cannot be compared with anything. - **Taking an extrapolation for a forecast**. Neudata's ranges for 2030 are the 2024 range grown at 21% a year; the consultancies' figures, a rate applied to a base. None of them is a model of the market. - **Mixing reports**. The 2024 figure in the 2025 report (at least 2.5 billion) and the one implied by the 2026 report (2.39 billion) come from different catalogs. A growth rate computed between them means nothing. - **Confusing spending with value**. That a lot is spent on a type of data does not mean it works: Neudata counts what is paid, not what is earned. Lessons 6.7.4 and 6.7.5 cover how a dataset is evaluated. - **Ignoring the internal cost**. The license price is only one part: ingestion, mapping to companies, quality control and research cost as much or more, and they do not scale with the size of the portfolio. ## Exercises 1. Neudata gives a minimum spending of 2.5 billion in 2024, 11.4 billion if the short profiles are added and a range of 2.7 to 12.7 billion if the catalog is 90% of spending. Reconstruct the range and explain the difference at the low end. **Solution.** ```python for v in (2.5, 11.4): print(f"{v} / 0.9 = {v / 0.9:.2f}") v = np.arange(2400, 2600) / 1000 ok = v[(np.round(v, 1) == 2.5) & (np.round(v / 0.9, 1) == 2.7)] print(f"rounds to 2.5 and gives 2.7: from {ok.min()} to {ok.max()}") ``` ```output 2.5 / 0.9 = 2.78 11.4 / 0.9 = 12.67 rounds to 2.5 and gives 2.7: from 2.451 to 2.474 ``` The high end matches (11.4 / 0.9 = 12.67 billion). The low end would give 2.78 billion, which would round to 2.8, not to 2.7. The two roundings only match if the published “2.5” is a figure of about 2.450 to 2.474 billion: it rounds to 2.5 and, divided by 0.9, gives 2.7. Moral: the rounded figures in a press release do not always allow you to reconstruct the decimals of the next one; you reconstruct the logic, not every digit. 2. A vendor offers you a dataset for 400,000 dollars a year and estimates the cost of integrating it at 300,000. Your fund manages 2 billion, but the signal only affects a third of the portfolio (the stocks the dataset covers). How many basis points does it have to deliver on that third to pay for itself? **Solution.** ```python coste = 0.4e6 + 0.3e6 cubierta = 2e9 / 3 print(f"on the whole portfolio: {coste / 2e9 * 1e4:.1f} bp") print(f"on the covered part: {coste / cubierta * 1e4:.1f} bp") ``` ```output on the whole portfolio: 3.5 bp on the covered part: 10.5 bp ``` 3.5 bp on the whole portfolio, which is 10.5 bp on the third the signal moves. This is the figure to compare with the signal's backtest (lesson 6.7.5), after transaction costs and with its standard error. 3. Pick an alternative data market report that is not in the table. Note what it measures, the base year, the rate, the period and whether it publishes its method. How many times is the market multiplied over its horizon at that rate? Is it compatible with the growth measured by Neudata? **Solution.** The lesson's template works: firm and date of the page, definition of the market (who buys and what), base-year value, final value, rate and period, and whether the method is available without paying. With rate g and n years, the market is multiplied by $(1 + g)^n$: at 40% for eight years, by 14.8. If that rate is double the highest one measured by Neudata (33% in 2024), the figure needs a justification that the press release almost never provides. Also check that base value, rate and final value add up: in the table, Precedence shows a jump of 52.6% from 2025 to 2026 and a rate of 50.68% afterwards, and its reconstructed 2035 is within 1% of the published value. [^1]: Neudata, “How big is the alternative data market for investment managers?” (, accessed on 2026-10-23; the page is undated and refers to the 2025 report): “we included 2,338 datasets in our analysis”; “((max−min)×0.3)+min”; “if a dataset was rated as having 6-10 investment clients, we assumed eight distinct buyers”; growth of 15%, 13%, 26% and 33% from 2021 to 2024; “the average dataset (as of 2024) earns \$1.1m/yr”; sensitivities of 11.4 billion (*Lite* profiles) and of 2.7 to 12.7 billion (catalog = 90%); “the margin for error is large” and “a proxy for alternative data spending rather than a precise measure”. [^2]: Neudata, press release “Alternative data spending by investment management firms could top \$15.4bn in 2025”, London, 2025-02-26 (, accessed on 2026-10-23): “a minimum of \$2.5 billion […] in 2024”, average growth of 21% a year since 2020, 33% in 2024, “nearly 8,000 unique datasets” in its catalog, and from 8.6 to 39.9 billion in 2030. [^3]: Neudata, “Alternative data market trends in 2026” (, 2026-02-24, accessed on 2026-10-23): “approximately \$2.8bn in 2025, growing 17% year-on-year”; “2,805 datasets in 2025, up from 2,215 in 2024”; “approximately \$23.1bn by 2030 if current growth trends persist”. The page does not give the base of that projection. [^4]: Pages accessed on 2026-10-23: Grand View Research (, “October 2024”); IMARC (, “Last Updated: October 05, 2026”); Technavio (, “Published: Jan 2025”); Precedence Research (, “Last Updated: 17 Sep 2026”); Arizton (, published in November 2024 and updated in February 2026). Only the figures from the public part of each page are used. [^5]: Neudata, press release “AI adoption is rising, but alt-data vendor spend hasn't” (London, 2026-02-23, , accessed on 2026-10-23): “around 20 investment clients on average, down from 25 in 2024”. ## Self-assessment 1. Neudata estimates that investment managers spent about 2.8 billion dollars on alternative data in 2025; a consultancy publishes a “market” of 14.16 billion that same year. What is the most likely explanation for the difference? - A. One of the two made an addition mistake - B. The consultancy uses dollars from another year - C. They measure different things: Neudata, the spending of investment managers on the datasets in its catalog; the consultancy, a market it defines itself, with more buyer sectors and a method it does not publish openly - D. Neudata only counts Europe *Answer C: They measure different things: Neudata, the spending of investment managers on the datasets in its catalog; the consultancy, a market it defines itself, with more buyer sectors and a method it does not publish openly.* The figures are not comparable without their definition. Neudata explains its method (catalog prices times number of clients) and limits it to investment managers; the consultancies include other users (banking, retail…) and do not detail the calculation in the public part of their reports. 2. A report says the market will grow at a compound annual rate of 52.5% for five years. What does that imply? - A. That it is multiplied by about 8.25 times in five years - B. That it grows 52.5% in total - C. That it doubles in five years - D. That it grows 10.5 points every year *Answer A: That it is multiplied by about 8.25 times in five years.* 1.525 to the power of 5 is 8.25. Neudata measured growth of 13% to 33% a year between 2021 and 2025: with a sustained 21%, spending is multiplied by 2.6 in five years, not by 8. 3. In Neudata's method, when a vendor gives a price range, which price is taken? - A. The maximum, because it is the list price - B. The midpoint of the range - C. The minimum, to be prudent - D. The one at 30% of the range from the minimum, because actual transactions are concentrated below the midpoint *Answer D: The one at 30% of the range from the minimum, because actual transactions are concentrated below the midpoint.* Neudata uses ((max − min) × 0.3) + min and says it checked this with buyers: negotiated prices fall below the midpoint. With the midpoint, the total would be higher. 4. A 500 million dollar fund is considering a dataset costing 1.1 million a year plus 0.9 million of internal cost (data, engineering, analysis). What additional return does it have to deliver just to pay for itself? - A. 4 bp a year - B. 40 bp a year - C. 0.4 bp a year - D. 400 bp a year *Answer B: 40 bp a year.* 2 million / 500 million = 0.4% = 40 bp a year, before any profit. For a 20 billion fund it would be 1 bp: that is why expensive data is concentrated in large portfolios. 5. What question should you ask first when faced with a market size figure? - A. Who publishes it and how many times it has been cited - B. Whether it is in dollars or euros - C. What it measures (whose spending, on what), with what method, in what base year and whether its growth rate is consistent with what has been observed - D. Whether it is the highest figure available *Answer C: What it measures (whose spending, on what), with what method, in what base year and whether its growth rate is consistent with what has been observed.* Definition, method, base year and consistency of the rate are the four filters. The popularity of a figure says nothing about its quality: consultancies' press releases are reproduced without the method. ## Key points - Alternative data is whatever is not prices, volumes or financial statements: traces left by people (cards, searches, reviews), by company processes (job postings, patents, shipments) and by sensors (satellite, AIS, weather). - Neudata estimates investment managers' spending at a minimum of 2.5 billion dollars in 2024 and 2.8 billion in 2025 (+17%), with measured growth of 13% to 33% a year since 2021. - Its method is explicit: the price of each dataset in its catalog (at 30% of the range) times the number of clients. The 2025 and 2030 ranges in the 2025 report come from multiplying the 2024 range by 1.21 each year. - The consultancies publish markets of 11.7 to 14.6 billion in 2025 and of 52.2 to 135.7 billion in 2030, with rates of 34% to 63% a year: they measure something else and do not publish their method openly. - For the buyer, the useful figure is a different one: the cost of a dataset divided by the size of the portfolio. A dataset costing 2 million a year requires 40 bp from a 500 million fund and 1 bp from a 20 billion one. ## Resources ### Documentation - [Neudata: How big is the alternative data market for investment managers? (method and 2024 figures)](https://www.neudata.co/education/how-big-is-the-alternative-data-market-for-investment-managers) · neudata.co - [Neudata: press release of February 26, 2025 (2.5 billion in 2024; 2025 and 2030 ranges)](https://www.neudata.co/press-release/alternative-data-spending-by-investment-management-firms-could-top-15-4bn-in-2025-according-to-neudata-s-latest-report) · neudata.co - [Neudata: Alternative data market trends in 2026 (2.8 billion in 2025)](https://www.neudata.co/blog/state-of-the-alternative-data-market-2026) · neudata.co - [Neudata: press release of February 23, 2026 (2,805 datasets, 20 clients per dataset)](https://www.neudata.co/press-release/ai-adoption-is-rising-but-alt-data-vendor-spend-hasn-t-neudata-report-finds) · neudata.co - [Grand View Research: Alternative Data Market (October 2024 release)](https://www.grandviewresearch.com/press-release/global-alternative-data-market) · grandviewresearch.com - [Precedence Research: Alternative Data Market (updated September 17, 2026)](https://www.precedenceresearch.com/alternative-data-market) · precedenceresearch.com ### Data - [Level 6 data: quarterly ground truth of the alternative data (CSV)](https://pythonparatrading.com/curso/datos/nivel-6/alt6_verdad_trimestral.csv) · Download · alt6_verdad_trimestral.csv Previous lesson: [6.6.5 · Cloud storage, lakehouse (Delta, Iceberg) and GPU (RAPIDS, cuDF)](https://pythonparatrading.com/en/python-trading-course/level-6/cloud-storage-lakehouse-and-gpu/) Next lesson: [6.7.2 · Types of alternative data: what each one measures](https://pythonparatrading.com/en/python-trading-course/level-6/types-of-alternative-data/)